Initial commit

HOWTO.md

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+# HOWTO Interactive Mode
+
+## Start server
+
+### UniCRS
+
+Start the server with the following command (RedDial dataset):
+
+```bash
+python -m script.serve_model --crs_model unicrs --kg_dataset redial --model microsoft/DialoGPT-small --rec_model data/models/unicrs_rec_redial/ --conv_model data/models/unicrs_conv_redial/ --context_max_length 128 --entity_max_length 43 --tokenizer_path microsoft/DialoGPT-small --text_tokenizer_path roberta-base --resp_max_length 128 --text_encoder roberta-base --debug
+```
+
+### BARCOR
+
+Start the server with the following command (RedDial dataset):
+
+```bash
+python -m script.serve_model --crs_model barcor --kg_dataset redial --hidden_size 128 --entity_hidden_size 128 --num_bases 8  --context_max_length 200 --entity_max_length 32 --rec_model data/models/barcor_rec_redial/ --conv_model data/models/barcor_conv_redial/ --tokenizer_path facebook/bart-base --encoder_layers 2 --decoder_layers 2 --attn_head 2 --text_hidden_size 300 --resp_max_length 128 --debug
+```
+
+### KBRD
+
+Start the server with the following command (RedDial dataset):
+
+```bash
+python -m script.serve_model --crs_model kbrd --kg_dataset redial --hidden_size 128 --entity_hidden_size 128 --num_bases 8  --context_max_length 200 --entity_max_length 32 --rec_model data/models/kbrd_rec_redial/ --conv_model data/models/kbrd_conv_redial/ --tokenizer_path facebook/bart-base --encoder_layers 2 --decoder_layers 2 --attn_head 2 --text_hidden_size 300 --resp_max_length 128
+```
+
+### ChatGPT
+
+Start the server with the following command (RedDial dataset):
+
+```bash
+python -m script.serve_model --api_key {API_KEY} --kg_dataset redial --crs_model chatgpt
+```
+
+Note that the item embeddings should be computed before starting the server and stored in the `data/embed_items/{kg_dataset}` folder.
+
+## Communicate with the server
+
+Test in the terminal with the following command:
+
+```python
+import requests
+
+url = "http://127.0.0.1:5005/"
+s = requests.Session() 
+
+context = []
+data = {
+    "context": context,
+    "message": "Hi I am looking for a movie like Super Troopers (2001)",
+}
+
+response = s.post(url, json=data)
+print(response.status_code)
+print(response.json())
+
+response = response.json()
+
+context += ["Hi I am looking for a movie like Super Troopers (2001)", response.get("response")]
+data = {
+    "context": context,
+    "message": "I love action movies",
+}
+
+response = s.post(url, json=data)
+```
+
+## Start Streamlit app
+
+A Streamlit is available to collect conversational data from users. The idea is to put two models in competition and ask the best model based on the user's feedback.
+
+```bash
+python -m streamlit run crs_arena/arena.py
+```
+
+The configuration of the CRSs are in the `data/arena/crs_config/` folder. The available models with their associated configuration are defined in `CRS_MODELS` in `crs_arena/battle_manager.py`.
+
+The conversation logs are stored in the `data/arena/conversation_logs/` folder. The votes are registered in the `data/arena/vote.db` SQLite database.

LICENSE

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+MIT License
+
+Copyright (c) 2023 Xiaolei Wang, Xinyu Tang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README_git.md

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+# Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
+
+This repo provides the source code & data of our paper: [Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models](https://arxiv.org/abs/2305.13112) (Arxiv 2023).
+
+## 😀 Overview
+
+**Highlights**:
+  - 1️⃣ We are the first to examine ChatGPT in conversational recommendation systematically, the ability of which is **underestimated** in traditional evaluation approach.
+  - 💡 We propose a new interactive approach that employs LLM-based user simulators for evaluating CRSs.
+  - 🔝 The recall@50 metric can be boosted from 0.218 to 0.739 on the redial dataset with our new interactive evaluation approach, even surpassing the currently leading CRS baseline.
+
+we propose an **i**nteractive **Eval**uation approach based on **LLM**s named **iEvaLM** that harnesses LLM-based user simulators. We take the ground-truth items from the example as the user preference through the interaction, and use them to set up the persona of the simulated users by LLMs through instructions. To further make a comprehensive evaluation, we consider two types of interaction: *attribute-based question answering* and *free-form chit-chat*.
+
+<p align="center">
+  <img src="./asset/eval.png" width="75%" height="75% title="Overview of iEvaLM-CRS" alt="">
+</p>
+
+## 🚀 Quick Start
+
+### Requirements
+
+  - python==3.9.16
+  - torch==1.13.1
+  - transformers==4.28.1
+  - torch_geometric==2.3.0
+  - accelerate==0.18.0
+
+### Download Models
+
+You can download our fine-tuned models from the [link](https://drive.google.com/drive/folders/1h2AcRn3cd9qXToM9hFAXFV5evXJM-wyD?usp=sharing), which include recommendation and conversation models of **KBRD**, **BARCOR** and **UniCRS**. Please put the downloaded model into src/utils/model directory.
+
+### Interact with the user simulator
+
+  - dataset: [redial, opendialkg]
+  - mode: [ask, chat]
+  - model: [kbrd, barcor, unicrs, chatgpt]
+
+```bash
+cd script
+bash {dataset}/cache_item.sh 
+bash {dataset}/{mode}_{model}.sh 
+```
+
+You can customize your iEvaLM-CRS by specifying these configs:
+  - `--api_key`: your API key
+  - `--turn_num`: number of conversation turns. We employ five-round interaction in iEvaLM-CRS.
+
+After the execution, you will find detailed interaction information under "save_{turn_num}/{mode}/{model}/{dataset}/".
+
+### Evaluate
+
+```bash
+cd script
+bash {dataset}/Rec_eval.sh
+```
+
+You can customize your iEvaLM-CRS by specifying these configs:
+  - `--turn_num`: number of conversation turns.
+  - `--mode`: [ask, chat]
+
+After the execution, you will find evaluation results under "save_{turn_num}/result/{mode}/{model}/{dataset}.json".
+
+## 🌟 Perfermance
+
+<p align="center">Performance of CRSs and ChatGPT using different evaluation approaches.</p>
+<table border="1" align="center">
+  <tbody >
+  <tr align="center">
+    <td colspan="2">Model</td>
+    <td colspan="3">KBRD</td>
+    <td colspan="3">BARCOR</td>
+    <td colspan="3">UniCRS</td>
+    <td colspan="3">ChatGPT</td>
+  </tr>
+  <tr align="center">
+    <td colspan="2">Evaluation Approach</td>
+    <td>Original</td>
+    <td>iEvaLM(attr)</td>
+    <td>iEvaLM(free)</td>
+    <td>Original</td>
+    <td>iEvaLM(attr)</td>
+    <td>iEvaLM(free)</td>
+    <td>Original</td>
+    <td>iEvaLM(attr)</td>
+    <td>iEvaLM(free)</td>
+    <td>Original</td>
+    <td>iEvaLM(attr)</td>
+    <td>iEvaLM(free)</td>
+  </tr>
+  <tr align="center">
+    <td rowspan="3">ReDial</td>
+    <td>R@1</td>
+    <td>0.028</td>
+    <td>0.039</td>
+    <td>0.035</td>
+    <td>0.031</td>
+    <td>0.034</td>
+    <td>0.034</td>
+    <td>0.050</td>
+    <td>0.053</td>
+    <td>0.107</td>
+    <td>0.037</td>
+    <td><b>0.191</b></td>
+    <td>0.146</td>
+  </tr>
+  <tr align="center">
+    <td>R@10</td>
+    <td>0.169</td>
+    <td>0.196</td>
+    <td>0.198</td>
+    <td>0.170</td>
+    <td>0.201</td>
+    <td>0.190</td>
+    <td>0.215</td>
+    <td>0.238</td>
+    <td>0.317</td>
+    <td>0.174</td>
+    <td><b>0.536</b></td>
+    <td>0.440</td>
+  </tr>
+  <tr align="center">
+    <td>R@50</td>
+    <td>0.366</td>
+    <td>0.436</td>
+    <td>0.453</td>
+    <td>0.372</td>
+    <td>0.427</td>
+    <td>0.467</td>
+    <td>0.413</td>
+    <td>0.520</td>
+    <td>0.602</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  <tr align="center">
+    <td rowspan="3">OpenDialKG</td>
+    <td>R@1</td>
+    <td>0.231</td>
+    <td>0.131</td>
+    <td>0.234</td>
+    <td>0.312</td>
+    <td>0.264</td>
+    <td>0.314</td>
+    <td>0.308</td>
+    <td>0.180</td>
+    <td>0.314</td>
+    <td>0.310</td>
+    <td>0.299</td>
+    <td><b>0.400</b></td>
+  </tr>
+  <tr align="center">
+    <td>R@10</td>
+    <td>0.423</td>
+    <td>0.293</td>
+    <td>0.431</td>
+    <td>0.453</td>
+    <td>0.423</td>
+    <td>0.458</td>
+    <td>0.513</td>
+    <td>0.393</td>
+    <td>0.538</td>
+    <td>0.539</td>
+    <td>0.604</td>
+    <td><b>0.715</b></td>
+  </tr>
+  <tr align="center">
+    <td>R@50</td>
+    <td>0.492</td>
+    <td>0.377</td>
+    <td>0.509</td>
+    <td>0.510</td>
+    <td>0.482</td>
+    <td>0.530</td>
+    <td>0.574</td>
+    <td>0.458</td>
+    <td>0.609</td>
+    <td>-</td>
+    <td>-</td>
+    <td>-</td>
+  </tr>
+  </tbody>
+
+</table>
+
+<p align="center">Persuasiveness of explanations generated by CRSs and ChatGPT.</p>
+<table border="1" align="center">
+  <tbody>
+    <tr align="center">
+    <td>Model</td>
+    <td>Evaluation Approach</td>
+    <td>ReDial</td>
+    <td>OpenDialKG</td>
+  </tr>
+    <tr align="center">
+    <td rowspan="2">KBRD</td>
+    <td>Original</td>
+    <td>0.638</td>
+    <td>0.824</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td>iEvaLM</td>
+    <td>0.766</td>
+    <td>0.862</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td rowspan="2">BARCOR</td>
+    <td>Original</td>
+    <td>0.667</td>
+    <td>1.149</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td>iEvaLM</td>
+    <td>0.795</td>
+    <td>1.211</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td rowspan="2">UniCRS</td>
+    <td>Original</td>
+    <td>0.685</td>
+    <td>1.128</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td>iEvaLM</td>
+    <td>1.015</td>
+    <td>1.314</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td rowspan="2">ChatGPT</td>
+    <td>Original</td>
+    <td>0.787</td>
+    <td>1.221</td>
+  </tr>
+  </tr>
+    <tr align="center">
+    <td>iEvaLM</td>
+    <td><b>1.331</b></td>
+    <td><b>1.513</b></td>
+  </tr>
+  </tbody>
+</table>
+
+[CRSLab](https://github.com/RUCAIBox/CRSLab) will support this interactive evaluation approach, the results in our paper will be updated soon.
+
+## 📮 Contact
+
+If you have any questions for our paper or codes, please send an email to <[email protected]>.
+
+## 🐦 Citing
+
+Please cite the following paper if you find our code helpful.
+
+```bibtex
+@article{wang2023rethinking,
+  title={Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models},
+  author={Wang, Xiaolei and Tang, Xinyu and Zhao, Wayne Xin and Wang, Jingyuan and Wen, Ji-Rong},
+  journal={arXiv preprint arXiv:2305.13112},
+  year={2023}
+}
+```

crs_arena/arena.py

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+"""Streamlit app for side-by-side battle of two CRSs.
+
+The goal of this application is for a user to have a conversation with two
+conversational recommender systems (CRSs) and vote on which one they prefer.
+All the conversations are recorded and saved for future analysis. When the user
+arrives on the app, they are assigned a unique ID. Then, two CRSs are chosen
+for the battle depending on the number of conversations already recorded (i.e.,
+the CRSs with the least number of conversations are chosen). The user is given
+a few constraints on the items they are looking for, after they interact with
+the CRSs one after the other. The user can then vote on which CRS they prefer,
+upon voting a pop-up will appear giving the user the option to provide a more
+detailed feedback. Once the vote is submitted, all data is logged and the app
+restarts for a new battle.
+
+The app is composed of four sections:
+1. Title/Introduction
+2. Rules
+3. Side-by-Side Battle
+4. Feedback
+"""
+
+import asyncio
+import json
+import logging
+import os
+import threading
+import time
+from copy import deepcopy
+from datetime import datetime
+from typing import Dict, List
+
+import streamlit as st
+from battle_manager import (
+    CONVERSATION_COUNTS,
+    cache_fighters,
+    get_crs_fighters,
+    get_unique_user_id,
+)
+from crs_fighter import CRSFighter
+from utils import (
+    download_and_extract_item_embeddings,
+    download_and_extract_models,
+    upload_conversation_logs_to_hf,
+    upload_feedback_to_gsheet,
+)
+
+from src.model.crb_crs.recommender import *
+
+# A message is a dictionary with two keys: role and message.
+Message = Dict[str, str]
+
+logging.basicConfig(
+    level=logging.WARNING,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+# Download models and data externally stored if not already downloaded
+if not os.path.exists("data/models"):
+    logger.info("Downloading models...")
+    download_and_extract_models()
+if not os.path.exists("data/embed_items"):
+    logger.info("Downloading item embeddings...")
+    download_and_extract_item_embeddings()
+
+# Create the conversation logs directory
+CONVERSATION_LOG_DIR = "data/arena/conversation_logs/"
+os.makedirs(CONVERSATION_LOG_DIR, exist_ok=True)
+
+
+# Callbacks
+def record_vote(vote: str) -> None:
+    """Record the user's vote in the database.
+
+    Args:
+        vote: Voted CRS model name.
+    """
+    user_id = st.session_state["user_id"]
+    crs1_model: CRSFighter = st.session_state["crs1"]
+    crs2_model: CRSFighter = st.session_state["crs2"]
+    last_row_id = str(datetime.now())
+    asyncio.run(
+        upload_feedback_to_gsheet(
+            {
+                "id": last_row_id,
+                "user_id": user_id,
+                "crs1": crs1_model.name,
+                "crs2": crs2_model.name,
+                "vote": vote,
+            },
+            worksheet="votes",
+        )
+    )
+    feedback_dialog(row_id=last_row_id)
+
+
+def record_feedback(feedback: str, row_id: int) -> None:
+    """Record the user's feedback in the database and restart the app.
+
+    Args:
+        feedback: User's feedback.
+        vote: Voted CRS model name.
+        crs_models: Tuple of CRS model names.
+        user_id: Unique user ID.
+    """
+    asyncio.run(
+        upload_feedback_to_gsheet(
+            {"id": row_id, "feedback": feedback}, "feedback"
+        )
+    )
+
+
+def end_conversation(crs: CRSFighter, sentiment: str) -> None:
+    """Ends the conversation with given CRS model.
+
+    Records the conversation in the logs and moves either to the next CRS or
+    to the voting section.
+
+    Args:
+        crs: CRS model.
+        sentiment: User's sentiment (frustrated or satisfied).
+    """
+    messages: List[Message] = deepcopy(
+        st.session_state[f"messages_{crs.fighter_id}"]
+    )
+    messages.append({"role": "metadata", "sentiment": sentiment})
+    user_id = st.session_state["user_id"]
+    logger.info(f"User {user_id} ended conversation with {crs.name}.")
+    log_file_path = os.path.join(
+        CONVERSATION_LOG_DIR, f"{user_id}_{crs.name}.json"
+    )
+    with open(log_file_path, "a") as f:
+        json.dump(messages, f)
+
+    # Update the conversation count
+    CONVERSATION_COUNTS[crs.name] += 1
+
+    # Asynchronously save the conversation logs to Hugging Face Hub
+    asyncio.run(
+        upload_conversation_logs_to_hf(
+            log_file_path, f"conversation_logs/{user_id}_{crs.name}.json"
+        )
+    )
+
+    if crs.fighter_id == 1:
+        # Disable the chat interface for the first CRS
+        st.session_state["crs1_enabled"] = False
+        # Enable the chat interface for the second CRS
+        st.session_state["crs2_enabled"] = True
+    elif crs.fighter_id == 2:
+        # Disable the chat interface for the second CRS
+        st.session_state["crs2_enabled"] = False
+        # Enable the voting section
+        st.session_state["vote_enabled"] = True
+        # Scroll to the voting section
+
+    st.rerun()
+
+
+def get_crs_response(crs: CRSFighter, message: str):
+    """Gets the CRS response for the given message.
+
+    This method sends a POST request to the CRS model including the history of
+    the conversation and the user's message.
+
+    Args:
+        crs: CRS model.
+        message: User's message.
+
+    Yields:
+        Words from the CRS response.
+    """
+    history = deepcopy(st.session_state[f"messages_{crs.fighter_id}"])
+    history = history[:-1]  # Remove the last message (user's message)
+
+    response, state = crs.reply(
+        input_message=message,
+        history=st.session_state[f"messages_{crs.fighter_id}"],
+        options_state=st.session_state.get(f"state_{crs.fighter_id}", []),
+    )
+    st.session_state[f"state_{crs.fighter_id}"] = state
+    # response = "CRS response for testing purposes."
+    for word in response.split():
+        yield f"{word} "
+        time.sleep(0.05)
+
+
+@st.dialog("Your vote has been submitted!")
+def feedback_dialog(row_id: int) -> None:
+    """Pop-up dialog to provide feedback after voting.
+
+    Feedback is optional and can be used to provide more detailed information
+    about the user's vote.
+
+    Args:
+        row_id: Unique row ID of the vote.
+    """
+    feedback_text = st.text_area(
+        "(Optional) You can provide more detailed feedback below:"
+    )
+    if st.button("Finish", use_container_width=True):
+        record_feedback(feedback_text, row_id)
+        # Restart the app
+        st.session_state.clear()
+        st.rerun()
+
+
+# Streamlit app
+st.set_page_config(page_title="CRS Arena", layout="wide")
+
+# Cache some CRS fighters at startup
+cache_fighters(n=2)
+
+# Battle setup
+if "user_id" not in st.session_state:
+    st.session_state["user_id"] = get_unique_user_id()
+    st.session_state["crs1"], st.session_state["crs2"] = get_crs_fighters()
+
+
+# Introduction
+st.title(":gun: CRS Arena")
+st.write(
+    "Welcome to the CRS Arena! Here you can have a conversation with two "
+    "conversational recommender systems (CRSs) and vote on which one you "
+    "prefer."
+)
+
+st.header(":page_with_curl: Rules")
+st.write(
+    "* Chat with each CRS (one after the other) to get movie recommendations "
+    "up until you feel statisfied or frustrated.\n"
+    "* Try to send several messages to each CRS to get a better sense of their "
+    "capabilities. Don't quit after the first message!\n"
+    "* To finish chatting with a CRS, click on the button corresponding to "
+    "your feeling: frustrated or satisfied.\n"
+    "* Vote on which CRS you prefer or declare a tie.\n"
+    "* (Optional) Provide more detailed feedback after voting.\n"
+)
+
+# Side-by-Side Battle
+st.header(":point_down: Side-by-Side Battle")
+st.write("Let's start the battle!")
+
+# Initialize the chat messages
+if "messages_1" not in st.session_state:
+    st.session_state["messages_1"] = []
+    st.session_state["state_1"] = None
+if "messages_2" not in st.session_state:
+    st.session_state["messages_2"] = []
+    st.session_state["state_2"] = None
+
+if "crs1_enabled" not in st.session_state:
+    st.session_state["crs1_enabled"] = True
+if "crs2_enabled" not in st.session_state:
+    st.session_state["crs2_enabled"] = False
+if "vote_enabled" not in st.session_state:
+    st.session_state["vote_enabled"] = False
+
+col_crs1, col_crs2 = st.columns(2)
+
+# CRS 1
+with col_crs1:
+    with st.container(border=True):
+        st.write(":red_circle: CRS 1")
+        # Display the chat history
+        messages_crs1 = st.container(height=350, border=False)
+        for message in st.session_state["messages_1"]:
+            messages_crs1.chat_message(message["role"]).write(
+                message["message"]
+            )
+
+        if prompt1 := st.chat_input(
+            "Send a message to CRS 1",
+            key="prompt_crs1",
+            disabled=not st.session_state["crs1_enabled"],
+        ):
+            # Display the user's message
+            messages_crs1.chat_message("user").write(prompt1)
+
+            # Add user's message to chat history
+            st.session_state["messages_1"].append(
+                {"role": "user", "message": prompt1}
+            )
+            # Get the CRS response
+            response_crs1 = messages_crs1.chat_message(
+                "assistant"
+            ).write_stream(get_crs_response(st.session_state["crs1"], prompt1))
+
+            # Add CRS response to chat history
+            st.session_state["messages_1"].append(
+                {"role": "assistant", "message": response_crs1}
+            )
+
+        crs1_frustrated_col, crs1_satisfied_col = st.columns(2)
+        crs1_frustrated_col.button(
+            ":rage: Frustrated",
+            use_container_width=True,
+            key="end_frustated_crs1",
+            on_click=end_conversation,
+            kwargs={
+                "crs": st.session_state["crs1"],
+                "sentiment": "frustrated",
+            },
+            disabled=not st.session_state["crs1_enabled"],
+        )
+        crs1_satisfied_col.button(
+            ":heavy_check_mark: Satisfied",
+            use_container_width=True,
+            key="end_satisfied_crs1",
+            on_click=end_conversation,
+            kwargs={
+                "crs": st.session_state["crs1"],
+                "sentiment": "satisfied",
+            },
+            disabled=not st.session_state["crs1_enabled"],
+        )
+
+# CRS 2
+with col_crs2:
+    with st.container(border=True):
+        st.write(":large_blue_circle: CRS 2")
+        # Display the chat history
+        messages_crs2 = st.container(height=350, border=False)
+        for message in st.session_state["messages_2"]:
+            messages_crs2.chat_message(message["role"]).write(
+                message["message"]
+            )
+
+        if prompt2 := st.chat_input(
+            "Send a message to CRS 2",
+            key="prompt_crs2",
+            disabled=not st.session_state["crs2_enabled"],
+        ):
+            # Display the user's message
+            messages_crs2.chat_message("user").write(prompt2)
+
+            # Add user's message to chat history
+            st.session_state["messages_2"].append(
+                {"role": "user", "message": prompt2}
+            )
+
+            # Get the CRS response
+            response_crs2 = messages_crs2.chat_message(
+                "assistant"
+            ).write_stream(get_crs_response(st.session_state["crs2"], prompt2))
+
+            # Add CRS response to chat history
+            st.session_state["messages_2"].append(
+                {"role": "assistant", "message": response_crs2}
+            )
+
+        crs2_frustrated_col, crs2_satisfied_col = st.columns(2)
+        crs2_frustrated_col.button(
+            ":rage: Frustrated",
+            use_container_width=True,
+            key="end_frustated_crs2",
+            on_click=end_conversation,
+            kwargs={
+                "crs": st.session_state["crs2"],
+                "sentiment": "frustrated",
+            },
+            disabled=not st.session_state["crs2_enabled"],
+        )
+        crs2_satisfied_col.button(
+            ":heavy_check_mark: Satisfied",
+            use_container_width=True,
+            key="end_satisfied_crs2",
+            on_click=end_conversation,
+            kwargs={
+                "crs": st.session_state["crs2"],
+                "sentiment": "satisfied",
+            },
+            disabled=not st.session_state["crs2_enabled"],
+        )
+
+# Feedback section
+container = st.container()
+container.subheader(":trophy: Declare the winner!", anchor="vote")
+container_col1, container_col2, container_col3 = container.columns(3)
+container_col1.button(
+    ":red_circle: CRS 1",
+    use_container_width=True,
+    key="crs1_wins",
+    on_click=record_vote,
+    kwargs={"vote": st.session_state["crs1"].name},
+    disabled=not st.session_state["vote_enabled"],
+)
+container_col2.button(
+    ":large_blue_circle: CRS 2",
+    use_container_width=True,
+    key="crs2_wins",
+    on_click=record_vote,
+    kwargs={"vote": st.session_state["crs2"].name},
+    disabled=not st.session_state["vote_enabled"],
+)
+container_col3.button(
+    "Tie",
+    use_container_width=True,
+    key="tie",
+    on_click=record_vote,
+    kwargs={"vote": "tie"},
+    disabled=not st.session_state["vote_enabled"],
+)
+
+# Terms of service
+st.header("Terms of Service")
+st.write(
+    "By using this application, you agree to the following terms of service:\n"
+    "The service is a research platform. Please do not upload any private "
+    "information in the chat. The service collects the chat data and the user's"
+    " vote, which may be released under a Creative Commons Attribution (CC-BY) "
+    "or a similar license."
+)

crs_arena/battle_manager.py

@@ -0,0 +1,67 @@
+"""Battle Manager module.
+
+Contains helper functions to select fighters for a battle and generate
+unique user ids.
+"""
+
+import logging
+import uuid
+from collections import defaultdict
+from typing import Optional, Tuple
+
+from crs_fighter import CRSFighter
+from utils import get_crs_model
+
+# CRS models with their configuration files.
+CRS_MODELS = {
+    "kbrd_redial": "data/arena/crs_config/KBRD/kbrd_redial.yaml",
+    "kbrd_opendialkg": "data/arena/crs_config/KBRD/kbrd_opendialkg.yaml",
+    "unicrs_redial": "data/arena/crs_config/UniCRS/unicrs_redial.yaml",
+    "unicrs_opendialkg": "data/arena/crs_config/UniCRS/unicrs_opendialkg.yaml",
+    "barcor_redial": "data/arena/crs_config/BARCOR/barcor_redial.yaml",
+    "barcor_opendialkg": "data/arena/crs_config/BARCOR/barcor_opendialkg.yaml",
+    "chatgpt_redial": "data/arena/crs_config/ChatGPT/chatgpt_redial.yaml",
+    "chatgpt_opendialkg": (
+        "data/arena/crs_config/ChatGPT/chatgpt_opendialkg.yaml"
+    ),
+    "crbcrs_redial": "data/arena/crs_config/CRB_CRS/crb_crs_redial.yaml",
+}
+
+CONVERSATION_COUNTS = defaultdict(int).fromkeys(CRS_MODELS.keys(), 0)
+
+
+def get_crs_fighters() -> Tuple[CRSFighter, CRSFighter]:
+    """Selects two CRS models for a battle.
+
+    The selection is based on the number of conversations collected per model.
+    The ones with the least conversations are selected.
+
+    Returns:
+        CRS models to battle.
+    """
+    pair = sorted(CONVERSATION_COUNTS.items(), key=lambda x: x[1])[:2]
+    fighter1 = CRSFighter(1, pair[0][0], CRS_MODELS[pair[0][0]])
+    fighter2 = CRSFighter(2, pair[1][0], CRS_MODELS[pair[1][0]])
+    return fighter1, fighter2
+
+
+def get_unique_user_id() -> str:
+    """Generates a unique user id.
+
+    Returns:
+        Unique user id.
+    """
+    return str(uuid.uuid4())
+
+
+def cache_fighters(n: Optional[int] = None) -> None:
+    """Caches n CRS fighters.
+
+    Args:
+        n: Number of fighters to cache. If None, all fighters are cached.
+    """
+    logging.info(f"Caching {n} CRS fighters.")
+    for i, (model_name, config_path) in enumerate(CRS_MODELS.items()):
+        get_crs_model(model_name, config_path)
+        if n is not None and i == n:
+            break

crs_arena/crs_fighter.py

@@ -0,0 +1,125 @@
+"""CRS Fighter.
+
+This class represents a CRS fighter. A CRS fighter has a fighter id (i.e., 1
+or 2), a name (i.e., model name), and a CRS. The CRS is loaded using the
+model name and configuration file.
+"""
+
+import json
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
+
+from utils import get_crs_model
+
+from src.model.utils import get_entity, get_options
+
+if TYPE_CHECKING:
+    from battle_manager import Message
+
+
+class CRSFighter:
+    def __init__(self, fighter_id: int, name: str, config_path: str) -> None:
+        """Initializes CRS fighter.
+
+        Args:
+            fighter_id: Fighter id (1 or 2).
+            name: Model name.
+            config: Model configuration file.
+
+        Raises:
+            ValueError: If id is not 1 or 2.
+        """
+        if fighter_id not in [1, 2]:
+            raise ValueError("Fighter id must be 1 or 2.")
+
+        self.fighter_id = fighter_id
+
+        self.name = name
+        self.config_path = config_path
+        self.model = get_crs_model(self.name, self.config_path)
+
+        # Load entity data
+        self._load_entity_data()
+
+        # Load options
+        self.options = get_options(self.model.crs_model.kg_dataset)
+
+        # Generation arguments. Not used for now.
+        self.response_generation_args = {}
+
+    def _load_entity_data(self):
+        """Loads entity data."""
+        with open(
+            f"data/{self.model.crs_model.kg_dataset}/entity2id.json",
+            "r",
+            encoding="utf-8",
+        ) as f:
+            self.entity2id = json.load(f)
+
+        self.id2entity = {int(v): k for k, v in self.entity2id.items()}
+        self.entity_list = list(self.entity2id.keys())
+
+    def _process_user_input(
+        self, input_message: str, history: List["Message"]
+    ) -> Dict[str, Any]:
+        """Processes user input.
+
+        The conversation dictionary contains the following keys: context,
+        entity, rec, and resp. Context is a list of the previous utterances,
+        entity is a list of entities mentioned in the conversation, rec is the
+        recommended items, resp is the response generated by the model, and
+        template is the context with masked entities.
+        Note that rec, resp, and template are empty as the model is used for
+        inference only, they are kept for compatibility with the models.
+
+        Args:
+            input_message: User input message.
+            history: Conversation history.
+
+        Returns:
+            Processed user input.
+        """
+        context = [m["message"] for m in history] + [input_message]
+        entities = []
+        for utterance in context:
+            utterance_entities = get_entity(utterance, self.entity_list)
+            entities.extend(utterance_entities)
+
+        return {
+            "context": context,
+            "entity": entities,
+            "rec": [],
+            "resp": "",
+            "template": [],
+        }
+
+    def reply(
+        self,
+        input_message: str,
+        history: List["Message"],
+        options_state: Optional[List[float]],
+    ) -> Tuple[str, List[float]]:
+        """Generates a reply to the user input.
+
+        Args:
+            input_message: User input message.
+            history: Conversation history.
+            options_state: State of the options.
+
+        Returns:
+            Generated response and updated state.
+        """
+        # Process conversation to create conversation dictionary
+        conversation_dict = self._process_user_input(input_message, history)
+
+        if options_state is None or len(options_state) != len(self.options[1]):
+            options_state = [0.0] * len(self.options[1])
+
+        # Get response
+        response, state = self.model.get_response(
+            conversation_dict,
+            self.id2entity,
+            self.options,
+            options_state,
+            **self.response_generation_args,
+        )
+        return response, state

crs_arena/utils.py

@@ -0,0 +1,172 @@
+"""Utility functions for CRS Arena."""
+
+import asyncio
+import logging
+import os
+import sqlite3
+import sys
+import tarfile
+from datetime import timedelta
+from typing import Any, Dict, List
+
+import openai
+import pandas as pd
+import streamlit as st
+import wget
+import yaml
+from huggingface_hub import HfApi
+from streamlit_gsheets import GSheetsConnection
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from src.model.crs_model import CRSModel
+
+# Initialize Hugging Face API
+HF_API = HfApi(token=st.secrets.hf.hf_token)
+
+
+@st.cache_resource(show_spinner="Loading CRS...", max_entries=5)
+def get_crs_model(model_name: str, model_config_file: str) -> CRSModel:
+    """Returns a CRS model.
+
+    Args:
+        model_name: Model name.
+        model_config_file: Model configuration file.
+
+    Raises:
+        FileNotFoundError: If model configuration file is not found.
+
+    Returns:
+        CRS model.
+    """
+    logging.debug(f"Loading CRS model {model_name}.")
+    if not os.path.exists(model_config_file):
+        raise FileNotFoundError(
+            f"Model configuration file {model_config_file} not found."
+        )
+
+    model_args = yaml.safe_load(open(model_config_file, "r"))
+
+    if "chatgpt" in model_name:
+        openai.api_key = st.secrets.openai.api_key
+
+    # Extract crs model from name
+    name = model_name.split("_")[0]
+
+    return CRSModel(name, **model_args)
+
+
+def execute_sql_query(query: str, params: Dict[str, str]) -> List[Any]:
+    """Executes a SQL query with parameters.
+
+    Args:
+        query: SQL query.
+        params: Dictionary of parameters.
+
+    Returns:
+        Output of the query.
+    """
+    connection = sqlite3.connect(st.secrets.db.vote_db)
+    cursor = connection.cursor().execute(query, params)
+    output = cursor.fetchall()
+    connection.commit()
+    return output
+
+
+def download_and_extract_models() -> None:
+    """Downloads the models folder from the server and extracts it."""
+    logging.debug("Downloading models folder.")
+    models_url = st.secrets.files.models_folder_url
+    models_targz = "models.tar.gz"
+    models_folder = "data/models/"
+    try:
+        wget.download(models_url, models_targz)
+
+        logging.debug("Extracting models folder.")
+        with tarfile.open(models_targz, "r:gz") as tar:
+            tar.extractall(models_folder)
+
+        os.remove(models_targz)
+        logging.debug("Models folder downloaded and extracted.")
+    except Exception as e:
+        logging.error(f"Error downloading models folder: {e}")
+
+
+def download_and_extract_item_embeddings() -> None:
+    """Downloads the item embeddings folder from the server and extracts it."""
+    logging.debug("Downloading item embeddings folder.")
+    item_embeddings_url = st.secrets.files.item_embeddings_url
+    item_embeddings_tarbz = "item_embeddings.tar.bz2"
+    item_embeddings_folder = "data/"
+
+    try:
+        wget.download(item_embeddings_url, item_embeddings_tarbz)
+
+        logging.debug("Extracting item embeddings folder.")
+        with tarfile.open(item_embeddings_tarbz, "r:bz2") as tar:
+            tar.extractall(item_embeddings_folder)
+
+        os.remove(item_embeddings_tarbz)
+        logging.debug("Item embeddings folder downloaded and extracted.")
+    except Exception as e:
+        logging.error(f"Error downloading item embeddings folder: {e}")
+
+
+async def upload_conversation_logs_to_hf(
+    conversation_log_file_path: str, repo_filename: str
+) -> None:
+    """Uploads conversation logs to Hugging Face asynchronously.
+
+    Args:
+        conversation_log_file_path: Path to the conversation log file locally.
+        repo_filename: Name of the file in the Hugging Face repository.
+
+    Raises:
+        Exception: If an error occurs during the upload.
+    """
+    logging.debug(
+        "Uploading conversation logs to Hugging Face: "
+        f"{conversation_log_file_path}."
+    )
+    try:
+        await asyncio.get_event_loop().run_in_executor(
+            None,
+            lambda: HF_API.upload_file(
+                path_or_fileobj=conversation_log_file_path,
+                path_in_repo=repo_filename,
+                repo_id=st.secrets.hf.dataset_repo,
+                repo_type="dataset",
+            ),
+        )
+        logging.debug("Conversation logs uploaded to Hugging Face.")
+    except Exception as e:
+        logging.error(
+            f"Error uploading conversation logs to Hugging Face: {e}"
+        )
+
+
+async def upload_feedback_to_gsheet(
+    row: Dict[str, str], worksheet: str = "votes"
+) -> None:
+    """Uploads feedback to Google Sheets asynchronously.
+
+    Args:
+        row: Row to upload to the worksheet.
+        worksheet: Name of the worksheet to upload the feedback to.
+
+    Raises:
+        Exception: If an error occurs during the upload.
+    """
+    logging.debug("Uploading feedback to Google Sheets.")
+    try:
+        gs_connection = st.connection("gsheets", type=GSheetsConnection)
+        df = gs_connection.read(worksheet=worksheet)
+        if df[df["id"] == row["id"]].empty:
+            df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
+        else:
+            # Add feedback to existing row
+            df.loc[df["id"] == row["id"], "feedback"] = row["feedback"]
+        gs_connection.update(data=df, worksheet=worksheet)
+        logging.debug("Feedback uploaded to Google Sheets.")
+    except Exception as e:
+        logging.error(f"Error uploading feedback to Google Sheets: {e}")

data/arena/crs_config/BARCOR/barcor_opendialkg.yaml

@@ -0,0 +1,8 @@
+kg_dataset: opendialkg
+context_max_length: 200
+rec_model: data/models/barcor_rec_opendialkg
+conv_model: data/models/barcor_conv_opendialkg
+tokenizer_path: facebook/bart-base
+resp_max_length: 128
+seed: 42
+debug: false

data/arena/crs_config/BARCOR/barcor_redial.yaml

@@ -0,0 +1,8 @@
+kg_dataset: redial
+context_max_length: 200
+rec_model: data/models/barcor_rec_redial/
+conv_model: data/models/barcor_conv_redial/
+tokenizer_path: facebook/bart-base
+resp_max_length: 128
+seed: 42
+debug: false

data/arena/crs_config/CRB_CRS/crb_crs_redial.yaml

@@ -0,0 +1,5 @@
+dataset: "redial"
+domain: "movies"
+corpus_folder: "data/redial/corpus"
+mle_model_path: "data/models/crb_crs_redial/mle_model.pkl"
+recommender_path: "data/models/crb_crs_redial/movie_recommender.pkl"

data/arena/crs_config/ChatGPT/chatgpt_opendialkg.yaml

@@ -0,0 +1,3 @@
+kg_dataset: opendialkg
+seed: 42
+debug: false

data/arena/crs_config/ChatGPT/chatgpt_redial.yaml

@@ -0,0 +1,3 @@
+kg_dataset: redial
+seed: 42
+debug: false

data/arena/crs_config/KBRD/kbrd_opendialkg.yaml

@@ -0,0 +1,16 @@
+kg_dataset: opendialkg
+hidden_size: 128
+entity_hidden_size: 128
+num_bases: 8
+context_max_length: 200
+entity_max_length: 32
+rec_model: data/models/kbrd_rec_opendialkg/
+conv_model: data/models/kbrd_conv_opendialkg/
+tokenizer_path: facebook/bart-base
+encoder_layers: 2
+decoder_layers: 2
+attn_head: 2
+text_hidden_size: 300
+resp_max_length: 128
+seed: 42
+debug: false

data/arena/crs_config/KBRD/kbrd_redial.yaml

@@ -0,0 +1,16 @@
+kg_dataset: redial
+hidden_size: 128
+entity_hidden_size: 128
+num_bases: 8
+context_max_length: 200
+entity_max_length: 32
+rec_model: data/models/kbrd_rec_redial/
+conv_model: data/models/kbrd_conv_redial/
+tokenizer_path: facebook/bart-base
+encoder_layers: 2
+decoder_layers: 2
+attn_head: 2
+text_hidden_size: 300
+resp_max_length: 128
+seed: 42
+debug: false

data/arena/crs_config/UniCRS/unicrs_opendialkg.yaml

@@ -0,0 +1,13 @@
+kg_dataset: opendialkg
+model: microsoft/DialoGPT-small
+rec_model: data/models/unicrs_rec_opendialkg/
+conv_model: data/models/unicrs_conv_opendialkg/
+context_max_length: 128
+entity_max_length: 20
+tokenizer_path: microsoft/DialoGPT-small
+text_tokenizer_path: roberta-base
+resp_max_length: 64
+text_encoder: roberta-base
+num_bases: 8
+seed: 42
+debug: false

data/arena/crs_config/UniCRS/unicrs_redial.yaml

@@ -0,0 +1,13 @@
+kg_dataset: redial
+model: microsoft/DialoGPT-small
+rec_model: data/models/unicrs_rec_redial/
+conv_model: data/models/unicrs_conv_redial/
+context_max_length: 128
+entity_max_length: 43
+tokenizer_path: microsoft/DialoGPT-small
+text_tokenizer_path: roberta-base
+resp_max_length: 128
+text_encoder: roberta-base
+num_bases: 8
+seed: 42
+debug: false

data/crb_crs/contractions.json

@@ -0,0 +1,121 @@
+{
+    "ain't": "are not",
+    "aren't": "are not",
+    "can't": "cannot",
+    "can't've": "cannot have",
+    "'cause": "because",
+    "could've": "could have",
+    "couldn't": "could not",
+    "couldn't've": "could not have",
+    "didn't": "did not",
+    "doesn't": "does not",
+    "don't": "do not",
+    "hadn't": "had not",
+    "hadn't've": "had not have",
+    "hasn't": "has not",
+    "haven't": "have not",
+    "he'd": "he would",
+    "he'd've": "he would have",
+    "he'll": " he will",
+    "he'll've": "he will have",
+    "he's": "he is",
+    "how'd": "how did",
+    "how'd'y": "how do you",
+    "how'll": "how will",
+    "how's": "how is",
+    "i'd": "I would",
+    "i'd've": "I would have",
+    "i'll": "I will",
+    "i'll've": "I will have",
+    "i'm": "I am",
+    "im": "I am",
+    "i've": "I have",
+    "isn't": "is not",
+    "it'd": "it would",
+    "it'd've": "it would have",
+    "it'll": "it will",
+    "it'll've": "it will have",
+    "it's": "it is",
+    "let's": "let us",
+    "ma'am": "madam",
+    "mayn't": "may not",
+    "might've": "might have",
+    "mightn't": "might not",
+    "mightn't've": "might not have",
+    "must've": "must have",
+    "mustn't": "must not",
+    "mustn't've": "must not have",
+    "needn't": "need not",
+    "needn't've": "need not have",
+    "o'clock": "of the clock",
+    "oughtn't": "ought not",
+    "oughtn't've": "ought not have",
+    "shan't": "shall not",
+    "sha'n't": "shall not",
+    "shan't've": "shall not have",
+    "she'd": "she would",
+    "she'd've": "she would have",
+    "she'll": "she will",
+    "she'll've": "she will have",
+    "she's": "she is",
+    "should've": "should have",
+    "shouldn't": "should not",
+    "shouldn't've": "should not have",
+    "so've": "so have",
+    "so's": "so is",
+    "that'd": "that had",
+    "that'd've": "that would have",
+    "that's": "that is",
+    "there'd": "there would",
+    "there'd've": "there would have",
+    "there's": "there is",
+    "they'd": "they would",
+    "they'd've": "they would have",
+    "they'll": "they will",
+    "they'll've": "they will have",
+    "they're": "they are",
+    "they've": "they have",
+    "to've": "to have",
+    "wasn't": "was not",
+    "we'd": "we would",
+    "we'd've": "we would have",
+    "we'll": "we will",
+    "we'll've": "we will have",
+    "we're": "we are",
+    "we've": "we have",
+    "weren't": "were not",
+    "what'll": "what will",
+    "what'll've": "what will have",
+    "what're": "what are",
+    "what's": "what is",
+    "what've": "what have",
+    "when's": "when is",
+    "when've": "when have",
+    "where'd": "where did",
+    "where's": "where is",
+    "where've": "where have",
+    "who'll": "who will",
+    "who'll've": "who will have",
+    "who's": "who is",
+    "who've": "who have",
+    "why's": "why is",
+    "why've": "why have",
+    "will've": "will have",
+    "won't": "will not",
+    "won't've": "will not have",
+    "would've": "would have",
+    "wouldn't": "would not",
+    "wouldn't've": "would not have",
+    "y'all": "you all",
+    "y'all'd": "you all would",
+    "y'all'd've": "you all would have",
+    "y'all're": "you all are",
+    "y'all've": "you all have",
+    "you'd": "you would",
+    "you'd've": "you would have",
+    "you'd've": "you would have",
+    "you'll": "you will",
+    "you'll've": "you will have",
+    "you're": "you are",
+    "you've": "you have"
+}

data/opendialkg/build_kg.py

@@ -0,0 +1,41 @@
+import json
+from collections import defaultdict
+
+kg = defaultdict(list)
+entity2id = defaultdict(lambda: len(entity2id))
+relation2id = defaultdict(lambda: len(relation2id))
+
+with open('id2info.json', encoding='utf-8') as f:
+    id2info = json.load(f)
+    for info_dict in id2info.values():
+        item = info_dict['name']
+        for attr, value in info_dict.items():
+            if attr == 'name':
+                continue
+            if isinstance(value, list):
+                for v in value:
+                    kg[entity2id[item]].append((relation2id[attr], entity2id[v]))
+            else:
+                kg[entity2id[item]].append((relation2id[attr], entity2id[value]))
+
+print(len(kg), len(entity2id), len(relation2id))
+
+with open('kg.json', 'w', encoding='utf-8') as f:
+    json.dump(kg, f, ensure_ascii=False)
+with open('entity2id.json', 'w', encoding='utf-8') as f:
+    json.dump(entity2id, f, ensure_ascii=False)
+with open('relation2id.json', 'w', encoding='utf-8') as f:
+    json.dump(relation2id, f, ensure_ascii=False)
+
+item_ids = set()
+with open('data.jsonl', encoding='utf-8') as f:
+    for line in f:
+        line = json.loads(line)
+        for turn in line['dialog']:
+            for item in turn['item']:
+                if item in entity2id:
+                    item_ids.add(entity2id[item])
+print(len(item_ids))
+item_ids = sorted(item_ids)
+with open('item_ids.json', 'w', encoding='utf-8') as f:
+    json.dump(item_ids, f, ensure_ascii=False)

data/opendialkg/item_ids.json

@@ -0,0 +1 @@
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data/opendialkg/relation2id.json

@@ -0,0 +1 @@
+{"year": 0, "actor": 1, "director": 2, "genre": 3, "language": 4, "writer": 5}

data/opendialkg/split.py

@@ -0,0 +1,137 @@
+import json
+import random
+from copy import copy
+from tqdm import tqdm
+
+
+def process_data(data_file_path):
+    global dialog_id
+
+    dialog_list = []
+    data_list = []
+    with open(data_file_path, encoding='utf-8') as f:
+        for line in tqdm(f):
+            line = json.loads(line)
+            context_text_list = []
+            context_text_template_list = []
+            context_entity_list = []
+            context_item_list = []
+            turn_id = 0
+
+            dialog = {'dialog_id': dialog_id, 'dialog': []}
+
+            for turn in line:
+                turn['turn_id'] = turn_id
+                dialog['dialog'].append(turn)
+
+                text = turn['text']
+                text_template = turn['text_template']
+                role = turn['role']
+                # item = [title2id[title] for title in turn['item']]
+                item = turn['item']
+                entity = turn['entity']
+
+                if role == 'assistant':
+                    flag = True
+                    if len(context_text_list) == 0:
+                        context_text_list.append('')
+                        turn_id += 1
+                        flag = False
+
+                    if len(item) > 0 and flag is True:
+                        data = {
+                            'dialog_id': dialog_id,
+                            'turn_id': turn_id,
+                            'context': copy(context_text_list),
+                            'context_template': copy(context_text_template_list),
+                            'context_entity': copy(context_entity_list),
+                            'context_item': copy(context_item_list),
+                            'resp': text,
+                            'item': item
+                        }
+                        data_list.append(data)
+                        # out_file.write(json.dumps(data, ensure_ascii=False) + '\n')
+
+                context_text_list.append(text)
+                context_text_template_list.append(text_template)
+                context_entity_list.extend(entity)
+                context_item_list.extend(item)
+                turn_id += 1
+
+            dialog_id += 1
+            dialog_list.append(dialog)
+
+    return data_list, dialog_list
+
+
+if __name__ == '__main__':
+    dialog_id = 0
+
+    random.seed(42)
+
+    with open('id2info.json', encoding='utf-8') as f:
+        id2info = json.load(f)
+    with open('title2id.json', encoding='utf-8') as f:
+        title2id = json.load(f)
+
+    data_file_path = 'dialog_movie.jsonl'
+    movie_data_list, movie_dialog_list = process_data(data_file_path)
+
+    data_file_path = 'dialog_Books.jsonl'
+    book_data_list, book_dialog_list = process_data(data_file_path)
+
+    all_dialog_list = movie_dialog_list + book_dialog_list
+    with open('data.jsonl', 'w', encoding='utf-8') as f:
+        for dialog in all_dialog_list:
+            f.write(json.dumps(dialog, ensure_ascii=False) + '\n')
+
+    all_data_list = movie_data_list + book_data_list
+    test_data_list = random.sample(all_data_list, int(len(all_data_list) * 0.15))
+    test_data_list = sorted(test_data_list, key=lambda x: x['dialog_id'])
+
+    print(len(test_data_list))
+
+    test_data_dialog_id_set = {f"{data['dialog_id']}_{data['turn_id']}" for data in test_data_list}
+    rest_data_list = []
+    for data in all_data_list:
+        if f"{data['dialog_id']}_{data['turn_id']}" not in test_data_dialog_id_set:
+            rest_data_list.append(data)
+    assert len(rest_data_list) + len(test_data_list) == len(all_data_list)
+
+    random.shuffle(rest_data_list)
+    train_data_list, valid_data_list = rest_data_list[int(0.15 * len(all_data_list)):], rest_data_list[:int(0.15 * len(all_data_list))]
+    assert len(valid_data_list) == len(test_data_list)
+
+    train_data_id_list = [f"{data['dialog_id']}_{data['turn_id']}" for data in train_data_list]
+    with open('train_data_id.json', 'w', encoding='utf-8') as f:
+        json.dump(train_data_id_list, f, ensure_ascii=False)
+
+    valid_data_id_list = [f"{data['dialog_id']}_{data['turn_id']}" for data in valid_data_list]
+    with open('valid_data_id.json', 'w', encoding='utf-8') as f:
+        json.dump(valid_data_id_list, f, ensure_ascii=False)
+
+    test_data_id_list = [f"{data['dialog_id']}_{data['turn_id']}" for data in test_data_list]
+    with open('test_data_id.json', 'w', encoding='utf-8') as f:
+        json.dump(test_data_id_list, f, ensure_ascii=False)
+
+    # with open('train_data.jsonl', 'w', encoding='utf-8') as f:
+    #     for data in train_data_list:
+    #         f.write(json.dumps(data, ensure_ascii=False) + '\n')
+    #
+    # with open('valid_data.jsonl', 'w', encoding='utf-8') as f:
+    #     for data in valid_data_list:
+    #         f.write(json.dumps(data, ensure_ascii=False) + '\n')
+    #
+    # with open('test_data.jsonl', 'w', encoding='utf-8') as f:
+    #     for data in test_data_list:
+    #         f.write(json.dumps(data, ensure_ascii=False) + '\n')
+
+    # cnt = 0
+    # with open('../../model/chat/data/opendialkg/test_data_processed.jsonl', encoding='utf-8') as f:
+    #     for line in f:
+    #         data = json.loads(line)
+    #         data_id = f"{data['dialog_id']}_{data['turn_id']}"
+    #         if data_id not in test_data_dialog_id_set:
+    #             print(data_id)
+    #         cnt += 1
+    # assert cnt == len(test_data_list)

data/opendialkg/test_data_id.json

@@ -0,0 +1 @@
+["3_1", "4_1", "11_3", "14_1", "19_1", "20_7", "31_1", "32_1", "37_1", "39_1", "44_1", "54_1", "56_1", "58_1", "65_3", "70_11", "70_1", "76_1", "79_3", "92_1", "102_1", "103_1", "103_3", "106_1", "110_1", "125_1", "131_1", "134_1", "135_1", "140_1", "145_1", "146_5", "147_3", "149_5", "154_5", "155_3", "159_1", "164_3", "165_3", "168_3", "169_5", "196_3", "207_5", "209_1", "215_5", "216_5", "218_1", "223_3", "228_1", "237_1", "241_1", "243_1", "248_3", "250_7", "252_1", "257_1", "274_7", "275_1", "277_5", "278_5", "280_5", "284_1", "294_1", "296_5", "303_5", "308_5", "310_5", "314_1", "317_3", "318_1", "319_3", "327_5", "330_1", "337_1", "344_1", "346_1", "348_1", "354_1", "359_3", "367_3", "370_1", "372_1", "372_5", "373_1", "374_1", "375_5", "377_3", "377_1", "380_1", "383_5", "383_3", "385_3", "389_3", "391_1", "393_3", "405_3", "406_1", "410_1", "417_1", "425_5", "429_5", "431_3", "433_1", "439_7", "439_3", "451_1", "456_1", "458_7", "463_1", "466_1", "488_3", "496_3", "499_1", "500_3", "501_1", "502_1", "503_3", "507_1", "522_1", "523_1", "526_5", "528_5", "540_3", "543_1", "546_3", "552_5", "554_5", "556_1", "557_7", "557_3", "561_1", "571_1", "575_3", "575_7", "577_1", "582_1", "583_1", "587_3", "588_1", "594_9", "595_1", "597_3", "610_1", "613_3", "613_1", "616_5", "617_1", "618_1", "635_5", "636_3", "639_5", "641_3", "643_1", "657_1", "659_1", "661_5", "664_5", "665_5", "670_5", "670_1", "670_7", "673_1", "686_3", "687_1", "693_3", "694_1", "699_1", "702_1", "709_1", "720_1", "726_5", "731_1", "743_1", "768_1", "769_3", "773_1", "776_7", "783_5", "792_3", "793_5", "794_1", "794_7", "801_3", "803_1", "806_3", "819_1", "821_3", "822_5", "830_3", "830_1", "832_1", "833_3", "844_1", "850_3", "851_1", "857_1", "860_1", "864_1", "868_1", "875_1", "883_3", "896_1", "903_5", "917_1", "927_1", "929_3", "934_3", "943_1", "944_1", "948_1", "956_3", "957_1", "967_3", "973_1", "976_1", "980_1", "1001_1", "1003_5", "1003_3", "1003_1", "1006_1", "1007_3", "1008_5", "1010_1", "1011_5", "1014_11", "1021_1", "1022_3", "1033_5", "1038_5", "1040_3", "1041_1", "1042_3", "1043_5", "1043_3", "1051_3", "1066_1", "1072_3", "1086_3", "1098_1", "1101_3", "1102_3", "1105_5", "1108_1", "1111_1", "1113_1", "1114_3", "1118_1", "1120_7", "1122_1", "1123_1", "1124_1", "1127_1", "1132_1", "1136_1", "1143_1", "1143_5", "1147_1", "1148_1", "1150_3", "1150_5", "1152_1", "1155_5", "1156_3", "1157_1", "1162_1", "1166_1", "1170_1", "1171_1", "1178_1", "1179_5", "1195_3", "1196_1", "1197_3", "1202_5", "1203_1", "1210_5", "1212_3", "1217_1", "1226_1", "1227_1", "1234_3", "1237_1", "1238_3", "1246_1", "1248_1", "1252_3", "1255_3", "1256_1", "1258_1", "1259_3", "1263_5", "1268_1", "1271_5", "1280_3", "1280_1", "1282_3", "1286_5", "1287_3", "1289_1", "1291_3", "1291_1", "1294_3", "1297_3", "1298_1", "1301_1", "1303_1", "1303_5", "1306_3", "1308_5", "1310_1", "1311_3", "1319_5", "1321_3", "1325_1", "1326_3", "1337_3", "1345_1", "1355_7", "1360_7", "1365_5", "1367_3", "1369_1", 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data/opendialkg/train_data_id.json

@@ -0,0 +1 @@
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data/opendialkg/valid_data_id.json

@@ -0,0 +1 @@
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"3195_1", "1103_1", "1613_1", "1602_1", "118_1", "4733_1", "4965_1", "1018_1", "2328_1", "1855_3", "74_1", "1785_3", "3422_3", "5127_3", "5163_3", "1369_5", "3019_1", "587_7", "2292_1", "4379_1", "1876_1", "1188_1", "1922_1", "1282_1", "71_3", "1968_1", "212_1", "3009_3", "1107_1", "3844_3", "2309_1", "4532_1", "149_1", "4554_5", "477_3", "4736_1", "2645_1", "2788_1", "1322_1", "1869_1", "3630_1", "25_3", "222_3", "113_3", "1350_1", "3024_1", "4922_3", "2008_3", "4101_5", "2275_1", "1631_5", "441_3", "780_5", "448_1", "1059_1", "4395_3", "3263_1", "4016_1", "3070_5", "2304_1", "1772_1", "365_1", "3694_1", "1009_1", "3744_3", "104_1", "3539_1", "2624_5", "4512_1", "5150_5", "1000_3", "263_1", "2835_7", "893_7", "2440_7", "2698_1", "4694_1", "3106_1", "1288_1", "5142_5", "4618_1", "3377_1", "1424_1", "593_1", "1620_5", "4672_3", "648_1", "594_7", "201_1", "3171_1", "3598_1", "4213_5", "4999_1", "2983_5", "4354_1", "4717_5", "2028_1", "2736_1", "900_1", "3727_3", "4873_1", "3116_1", "4871_5", "4495_1", "3352_1", "2917_1", "1510_1", "4717_1", "3544_1", "1436_1", "3989_1", "2687_7", "813_1", "3750_5", "4723_1", "3260_5", "3117_1", "1251_1", "1517_3", "2686_1", "3653_3", "3374_1", "3324_1", "1175_3", "2611_5", "1220_1", "4720_5", "4295_3", "479_1", "4888_3", "4921_5", "4231_1", "1069_3", "3006_7", "3733_1", "1544_5", "1222_5", "1655_3", "4624_1", "931_3", "5158_3", "3478_3", "50_3", "1703_1", "587_5", "3126_1", "163_1", "723_1", "3923_5", "4824_3", "2458_1", "795_5", "1805_5", "4046_7", "1573_3", "3629_3", "5034_1", "4421_1", "1741_1", "4761_3", "1828_3", "4531_5", "779_3", "257_3", "919_3", "74_3", "2104_1", "2286_5", "458_3", "591_1", "4108_1", "742_7", "2011_3", "3813_1", "3513_1", "877_3", "1976_5", "1169_3", "4498_1", "2299_3", "1030_5", "3993_3", "4517_1", "1144_1", "1547_3", "4770_1", "4060_1", "263_3", "1909_1", "4001_1", "458_1", "3359_5", "754_1", "653_1", "2782_3", "3794_1", "4325_3", "1534_1", "112_3", "4025_1", "3680_3", "2816_1", "4587_1", "4228_1", "1261_7", "219_1", "775_1", "4203_5", "7_3", "1868_3", "2038_5", "3840_5", "1398_5", "4475_3", "681_3", "1709_5", "2906_5", "4609_1", "1788_1", "4908_1", "3779_3", "733_3", "4046_1", "1576_1", "3819_1", "3920_3", "1002_1", "4011_1", "4281_1", "3305_3", "709_5", "4522_5", "5_3", "3053_1", "4287_1", "4579_7", "3335_5", "494_1", "3662_1", "352_1", "651_1", "1414_1", "2882_3", "3819_3", "4843_1", "3915_1", "680_7", "608_1", "4240_1", "111_1", "2213_1", "3670_1", "3998_1", "3426_3", "3638_3", "905_3", "607_1", "4685_3", "742_3", "4971_5", "2885_3", "1224_1", "3562_1", "1048_1", "1701_1", "1844_1", "4801_5", "2321_1", "418_1", "71_1", "1160_3", "4309_3", "3865_1", "3470_3", "88_3", "4302_3", "2158_1", "1028_1", "2621_5", "5044_5", "1779_3", "3300_1", "1338_3", "2544_3", "3238_1", "1185_3", "1690_1", "1614_3", "4496_3", "4279_1", "4122_1", "2722_3", "4975_3", "4345_5", "5148_7", "1428_3", "5120_5", "1781_5", "2667_5", "4473_1", "1026_3", "4028_1", "1574_3", "4677_1", "2763_1", "1159_5", "1018_3", "3096_7", "4536_1", "3718_3", "4259_3", "2134_5", "3274_3", "1553_7", "3107_5", "3410_3", "2194_1", "2647_3", "2508_1", "1372_3", "1694_1", "226_5", "1897_1", "2146_3", "2085_7", "4359_1", "1052_1", "3103_5", "3134_3", "150_3", "4388_1", "1032_1", "4440_1", "1531_7", "2294_5", "4989_3", "1364_3", "1454_1", "4795_3", "1207_1", "5121_5", "4583_1", "1278_5", "1089_1", "4311_1", "3789_3", "1333_1", "2097_1", "4227_1", "2256_3", "3406_1", "1982_5", "985_5", "4483_3", "3652_1", "1535_7", "4644_1", "1737_3", "455_1", "3793_3", "4943_1", "482_1", "312_1", "2536_5", "4524_1", "5144_1", "4406_3", "584_1", "1009_5", "3459_3", "613_5", "1833_1", "337_3", "2579_1", "1588_1", "2120_5", "4533_1", "258_1", "1247_3", "4451_5", "486_1", "4423_1", "347_1", "2483_1", "4092_3", "140_3", "3259_1", "3378_1", "598_1", "4739_1", "2679_5", "1110_1", "1821_1", "2511_1", "1208_1", "2533_5", "711_3", "2609_3", "2386_5", "2327_1", "323_1", "2937_1", "3581_5", "1040_1", "124_5", "3796_5", "1425_3", "391_3", "2575_3", "3461_3", "948_3", "871_5", "3960_1", "3278_3", "730_1", "1561_1", "572_9", "4191_3", "4224_1", "369_1", "1956_5", "1974_5", "4934_3", "292_1", "2343_1", "2675_3", "2053_1", "2566_3", "3168_3", "986_3", "1788_5", "3427_3", "3605_1", "5049_3", "3198_3", "3228_5", "779_9", "291_5", "3482_1", "2066_5", "503_1", "2257_1", "3724_3", "1357_3", "3596_3", "4403_9", "3018_1", "3248_1", "951_5", "2411_5", "3509_1", "3039_1", "522_3", "2520_7", "4543_3", "243_5", "4184_3", "647_5", "1257_1", "3887_3", "603_1", "1983_1", "2659_1", "5149_1", "2601_3", "240_5", "357_5", "1463_3", "51_3", "1773_3", "4274_1", "1813_7", "437_5", "2302_3", "1607_5", "3655_5", "1816_3", "562_3", "3763_1", "958_1", "250_1", "622_1", "2232_1", "4836_7", "3357_1", "802_1", "1153_1", "2750_3", "960_5", "4661_1", "4092_5", "3600_3", "1644_3", "3055_1", "2090_5", "741_3", "5010_3", "2409_3", "2183_1", "4046_5", "1006_3", "4639_3", "3888_1", "1497_1", "1925_1", "974_1", "4434_3", "4087_1", "1038_3", "1243_1", "1629_1", "84_1", "1403_1", "319_1", "3287_5", "93_1", "1798_1", "3880_1", "1038_1", "1288_3", "4401_3", "30_3", "224_1", "738_9", "3088_1", "413_1", "4411_3", "834_1", "2884_5", "334_1", "816_1", "2641_6", "1726_1", "2671_7", "4166_1", "3309_3", "2972_1", "3073_1", "1333_3", "5154_5", "2488_3", "4247_1", "4180_3", "85_5", "887_1", "4174_1", "3957_3", "3863_1", "1500_1", "370_7", "4349_3", "681_1", "1658_1", "683_1", "1552_1", "4205_5", "4409_1", "39_5", "2928_3", "3134_1", "4132_5", "2714_1", "5082_1", "1795_5", "1615_1", "4931_5", "4589_3", "1357_1", "3341_1", "2903_1", "1164_3", "4173_1", "4745_5", "2519_1", "4227_5", "1747_3"]

data/opendialkg_eval/process.py

@@ -0,0 +1,87 @@
+import json
+from copy import copy
+
+with open("../opendialkg/train_data_id.json", "r", encoding="utf-8") as f:
+    train_data_id = json.load(f)
+train_data_id_set = set(train_data_id)
+print(len(train_data_id_set))
+
+with open("../opendialkg/valid_data_id.json", "r", encoding="utf-8") as f:
+    valid_data_id = json.load(f)
+valid_data_id_set = set(valid_data_id)
+print(len(valid_data_id_set))
+
+with open("../opendialkg/test_data_id.json", "r", encoding="utf-8") as f:
+    test_data_id = json.load(f)
+test_data_id_set = set(test_data_id)
+print(len(test_data_id_set))
+
+with open("../opendialkg/data.jsonl", "r", encoding="utf-8") as f, open(
+    "train_data_processed.jsonl", "w", encoding="utf-8"
+) as train_w, open(
+    "valid_data_processed.jsonl", "w", encoding="utf-8"
+) as valid_w, open(
+    "test_data_processed.jsonl", "w", encoding="utf-8"
+) as test_w:
+    lines = f.readlines()
+    for line in lines:
+        dialog = json.loads(line)
+        context_list = []
+        entity_list = []
+        for message in dialog["dialog"]:
+            role = message["role"]
+            text = message["text"]
+            # mask_text = message['text_template']
+            entity_turn = message["entity"]
+            item_turn = message["item"]
+            dialog_turn_id = (
+                str(dialog["dialog_id"]) + "_" + str(message["turn_id"])
+            )
+
+            if dialog_turn_id in train_data_id_set:
+                data = {
+                    "dialog_id": dialog["dialog_id"],
+                    "turn_id": message["turn_id"],
+                    "context": copy(context_list),
+                    "entity": copy(entity_list),
+                    "rec": copy(item_turn),
+                    "resp": text,
+                }
+                train_w.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+            elif dialog_turn_id in valid_data_id_set:
+                data = {
+                    "dialog_id": dialog["dialog_id"],
+                    "turn_id": message["turn_id"],
+                    "context": copy(context_list),
+                    "entity": copy(entity_list),
+                    "rec": copy(item_turn),
+                    "resp": text,
+                }
+                valid_w.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+            elif dialog_turn_id in test_data_id_set:
+                data = {
+                    "dialog_id": dialog["dialog_id"],
+                    "turn_id": message["turn_id"],
+                    "context": copy(context_list),
+                    "entity": copy(entity_list),
+                    "rec": copy(item_turn),
+                    "resp": text,
+                }
+                test_w.write(json.dumps(data, ensure_ascii=False) + "\n")
+
+            context_list.append(text)
+            entity_list.extend(entity_turn)
+
+with open("train_data_processed.jsonl", "r", encoding="utf-8") as f:
+    lines = f.readlines()
+    print("train:", len(lines))
+
+with open("valid_data_processed.jsonl", "r", encoding="utf-8") as f:
+    lines = f.readlines()
+    print("valid:", len(lines))
+
+with open("test_data_processed.jsonl", "r", encoding="utf-8") as f:
+    lines = f.readlines()
+    print("test:", len(lines))

data/redial/item_ids.json

@@ -0,0 +1 @@
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1241, 1245, 1250, 1256, 1262, 1266, 1272, 1278, 1283, 1290, 1294, 1300, 1305, 1312, 1319, 1324, 1331, 1337, 1342, 1349, 1357, 1365, 1371, 1379, 1384, 1391, 1397, 1400, 1407, 1413, 1419, 1424, 1429, 1435, 1442, 1447, 1451, 1458, 1463, 1469, 1475, 1481, 1486, 1490, 1497, 1503, 1510, 1516, 1521, 1526, 1533, 1539, 1544, 1547, 1554, 1559, 1567, 1572, 1575, 1579, 1584, 1591, 1598, 1602, 1610, 1615, 1619, 1625, 1633, 1639, 1644, 1648, 1654, 1662, 1670, 1675, 1679, 1685, 1692, 1697, 1701, 1705, 1711, 1716, 1724, 1729, 1734, 1739, 1745, 1750, 1757, 1763, 1765, 1770, 1776, 1782, 1786, 1792, 1796, 1803, 1809, 1812, 1818, 1823, 1824, 1830, 1834, 1836, 1841, 1847, 1853, 1860, 1868, 1872, 1879, 1886, 1893, 1897, 1903, 1909, 1916, 1919, 1926, 1931, 1933, 1938, 1945, 1951, 1956, 1962, 1967, 1971, 1978, 1984, 1991, 1996, 1999, 2000, 2006, 2012, 2019, 2024, 2032, 2036, 2042, 2048, 2049, 2056, 2063, 2068, 2069, 2077, 2081, 2087, 2092, 2096, 2102, 2109, 2116, 2121, 2126, 2132, 2135, 2142, 2147, 2152, 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data/redial/relation2id.json

@@ -0,0 +1 @@
+{"genre": 0, "star": 1, "director": 2, "writer": 3}

data/redial/sample.json

@@ -0,0 +1,394 @@
+{
+  "movieMentions": {
+    "204334": "Happy Death Day  (2017)",
+    "125431": "Annabelle  (2014)",
+    "118338": "The Forest  (2016)",
+    "205430": "A Quiet Place (2018)",
+    "132562": "The Last House on the Left  (1972)",
+    "157765": "The Heat  (2013)",
+    "111776": "Super Troopers (2001)",
+    "187061": "Identity Thief (2013)",
+    "175203": "Hot Tub Time Machine",
+    "77161": "A Nightmare on Elm Street (1984)"
+  },
+  "respondentQuestions": {
+    "204334": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "125431": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "118338": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "205430": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "132562": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "187061": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "111776": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "157765": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "175203": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "77161": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    }
+  },
+  "messages": [
+    {
+      "senderWorkerId": 958,
+      "text": "Hello!",
+      "turn_id": 0,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "Hello!",
+      "word": []
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "Hello!",
+      "turn_id": 1,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "Hello!",
+      "word": []
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "What kind of movies do you like?",
+      "turn_id": 2,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "What kind of movies do you like?",
+      "word": [
+        "movies"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "I am looking for a movie recommendation. When I was younger I really enjoyed the @77161",
+      "turn_id": 3,
+      "entity": [],
+      "item": [
+        "A Nightmare on Elm Street (1984)"
+      ],
+      "thread": [
+        "A Nightmare on Elm Street (1984)"
+      ],
+      "masked_text": "I am looking for a movie recommendation. When I was younger I really enjoyed the <item>",
+      "word": [
+        "movie",
+        "recommendation",
+        "enjoyed"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "Oh, you like scary movies? I recently watched @204334",
+      "turn_id": 4,
+      "entity": [
+        "scary"
+      ],
+      "item": [
+        "Happy Death Day (2017)"
+      ],
+      "thread": [
+        "scary",
+        "Happy Death Day (2017)"
+      ],
+      "masked_text": "Oh, you like <genre> movies? I recently watched <item>",
+      "word": [
+        "scary",
+        "movies",
+        "watched"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "I also enjoyed watching @132562",
+      "turn_id": 5,
+      "entity": [],
+      "item": [
+        "The Last House on the Left (1972)"
+      ],
+      "thread": [
+        "The Last House on the Left (1972)"
+      ],
+      "masked_text": "I also enjoyed watching <item>",
+      "word": [
+        "enjoyed",
+        "watching"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "It was really good for a new \"scary movie\"",
+      "turn_id": 6,
+      "entity": [
+        "scary"
+      ],
+      "item": [],
+      "thread": [
+        "scary"
+      ],
+      "masked_text": "It was really good for a new \"<genre> movie\"",
+      "word": [
+        "scary",
+        "movie"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "I do enjoy some of the newer horror movies that I have seen as well.",
+      "turn_id": 7,
+      "entity": [
+        "horror"
+      ],
+      "item": [],
+      "thread": [
+        "horror"
+      ],
+      "masked_text": "I do enjoy some of the newer <genre> movies that I have seen as well.",
+      "word": [
+        "enjoy",
+        "horror",
+        "movies"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "I heard that @205430 is good. It is still in theaters though.",
+      "turn_id": 8,
+      "entity": [],
+      "item": [
+        "A Quiet Place (2018)"
+      ],
+      "thread": [
+        "A Quiet Place (2018)"
+      ],
+      "masked_text": "I heard that <item> is good. It is still in theaters though.",
+      "word": [
+        "heard",
+        "theaters"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "I really liked the movie @125431",
+      "turn_id": 9,
+      "entity": [],
+      "item": [
+        "Annabelle (2014)"
+      ],
+      "thread": [
+        "Annabelle (2014)"
+      ],
+      "masked_text": "I really liked the movie <item>",
+      "word": [
+        "movie"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "Me, too! It was really creepy, but I thought it was good!",
+      "turn_id": 10,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "Me, too! It was really creepy, but I thought it was good!",
+      "word": [
+        "creepy"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "Or @118338 I saw while in theaters, this was a very good movie. It had me on the edge of my seat for the whole show.",
+      "turn_id": 11,
+      "entity": [
+        "The Who"
+      ],
+      "item": [
+        "The Forest (2016)"
+      ],
+      "thread": [
+        "The Forest (2016)",
+        "The Who"
+      ],
+      "masked_text": "Or <item> I saw while in theaters, this was a very good movie. It had me on the edge of my seat for <person>le show.",
+      "word": [
+        "theaters",
+        "movie",
+        "edge",
+        "seat"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "I'm not sure if I saw that one, I'll have to check into it. Sounds familiar, but not sure. Thank you for your suggestions!",
+      "turn_id": 12,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "I'm not sure if I saw that one, I'll have to check into it. Sounds familiar, but not sure. Thank you for your suggestions!",
+      "word": [
+        "check",
+        "familiar",
+        "suggestions"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "Are there any comedies that you would suggest?",
+      "turn_id": 13,
+      "entity": [
+        "comedy"
+      ],
+      "item": [],
+      "thread": [
+        "comedy"
+      ],
+      "masked_text": "Are there any <genre> that you would suggest?",
+      "word": [
+        "comedies"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "Sure! I like comedies a lot. I like movies like @175203 and @111776 , but I also like @187061 and @157765 .",
+      "turn_id": 14,
+      "entity": [
+        "comedy"
+      ],
+      "item": [
+        "Hot Tub Time Machine",
+        "Super Troopers (2001)",
+        "Identity Thief (2013)",
+        "The Heat (2013)"
+      ],
+      "thread": [
+        "comedy",
+        "Hot Tub Time Machine",
+        "Super Troopers (2001)",
+        "Identity Thief (2013)",
+        "The Heat (2013)"
+      ],
+      "masked_text": "Sure! I like <genre> a lot. I like movies like <item> and <item> , but I also like <item> and <item> .",
+      "word": [
+        "comedies",
+        "lot",
+        "movies"
+      ]
+    },
+    {
+      "senderWorkerId": 959,
+      "text": "Wonderful! Thank you so much I think I am ready for movie night now.",
+      "turn_id": 15,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "Wonderful! Thank you so much I think I am ready for movie night now.",
+      "word": [
+        "ready",
+        "movie",
+        "night"
+      ]
+    },
+    {
+      "senderWorkerId": 958,
+      "text": "No problem! Thank you, too! :)",
+      "turn_id": 16,
+      "entity": [],
+      "item": [],
+      "thread": [],
+      "masked_text": "No problem! Thank you, too! :)",
+      "word": []
+    }
+  ],
+  "conversationId": "20041",
+  "respondentWorkerId": 958,
+  "initiatorWorkerId": 959,
+  "initiatorQuestions": {
+    "204334": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "125431": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "118338": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "205430": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "132562": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    },
+    "187061": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "111776": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "157765": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "175203": {
+      "suggested": 1,
+      "seen": 2,
+      "liked": 2
+    },
+    "77161": {
+      "suggested": 0,
+      "seen": 1,
+      "liked": 1
+    }
+  }
+}