| { | |
| "paper_id": "2022", | |
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| "date_generated": "2023-01-19T16:31:40.057617Z" | |
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| "title": "UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues", | |
| "authors": [ | |
| { | |
| "first": "Xinyan", | |
| "middle": [], | |
| "last": "Zhao", | |
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| "institution": "University of Science", | |
| "location": { | |
| "country": "Technology of China" | |
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| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Yasheng", | |
| "middle": [], | |
| "last": "Wang", | |
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| "affiliation": { | |
| "laboratory": "Huawei Noah's Ark Lab", | |
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| { | |
| "first": "Yitong", | |
| "middle": [], | |
| "last": "Li", | |
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| "laboratory": "Huawei Noah's Ark Lab", | |
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| { | |
| "first": "Fei", | |
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| "laboratory": "Huawei Noah's Ark Lab", | |
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| { | |
| "first": "Yajiao", | |
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| "last": "Liu", | |
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| "laboratory": "Huawei Noah's Ark Lab", | |
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| { | |
| "first": "Xin", | |
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| "last": "Jiang", | |
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| "laboratory": "Huawei Noah's Ark Lab", | |
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| { | |
| "first": "Qun", | |
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| "last": "Liu", | |
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| "affiliation": {}, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Huanhuan", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
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| "institution": "University of Science", | |
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| "country": "Technology of China" | |
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| "email": "[email protected]" | |
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| "abstract": "With the advances in deep learning, tremendous progress has been made with chitchat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, dialogue systems need to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chitchat and task-oriented dialogues. Besides, we propose a two-stage training method to train UniDS based on the unified dialogue data schema. UniDS does not need to adding extra parameters to existing chitchat dialogue systems. Experimental results demonstrate that the proposed UniDS works comparably well as the state-of-the-art chitchat dialogue systems and task-oriented dialogue systems. More importantly, UniDS achieves better robustness than pure dialogue systems and satisfactory switch ability between two types of dialogues. This work demonstrates the feasibility and potential of building a general dialogue system.", | |
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| "abstract": [ | |
| { | |
| "text": "With the advances in deep learning, tremendous progress has been made with chitchat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, dialogue systems need to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chitchat and task-oriented dialogues. Besides, we propose a two-stage training method to train UniDS based on the unified dialogue data schema. UniDS does not need to adding extra parameters to existing chitchat dialogue systems. Experimental results demonstrate that the proposed UniDS works comparably well as the state-of-the-art chitchat dialogue systems and task-oriented dialogue systems. More importantly, UniDS achieves better robustness than pure dialogue systems and satisfactory switch ability between two types of dialogues. This work demonstrates the feasibility and potential of building a general dialogue system.", | |
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| "section": "Abstract", | |
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| "text": "Dialogue system is an important tool to achieve intelligent user interaction, and it is actively studied by NLP and other communities. Current research of dialogue systems focus on task-oriented dialogue (TOD) systems (Hosseini-Asl et al., 2020; Peng et al., 2020; Yang et al., 2021) , achieving functional goals, and chit-chat dialogue systems aiming at entertainment (Zhou et al., 2018; Zhang et al., 2020; Zhao et al., 2020; . Different methods are devised for these two types of dialogue systems separately. However, a more suitable way for users would be to have one dialogue agent that is able to handle both chit-chat and TOD * This work was done during an internship at Huawei Noah's Ark Lab.", | |
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| "end": 245, | |
| "text": "(Hosseini-Asl et al., 2020;", | |
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| "text": "Peng et al., 2020;", | |
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| "text": "Yang et al., 2021)", | |
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| "end": 388, | |
| "text": "(Zhou et al., 2018;", | |
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| "text": "Zhang et al., 2020;", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| { | |
| "text": "I would like someone in the center.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| { | |
| "text": "I don't have much money...", | |
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| "section": "Does money buy happiness?", | |
| "sec_num": null | |
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| { | |
| "text": "I am looking for a place to stay that has cheap price range it should be in a type of hotel.", | |
| "cite_spans": [], | |
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| "section": "System", | |
| "sec_num": null | |
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| "text": "Depends how much money you spend on it.", | |
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| "sec_num": null | |
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| { | |
| "text": "Okay, do you have a specific area you want to stay in?", | |
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| "text": "Chit-chat Task in one conversation. As illustrated in Figure 1 , users may have communication-oriented needs (e.g. chatting about money and happiness) and task-oriented needs (e.g. hotel reservation) when interacting with a dialogue agent. Furthermore, inputs of dialogue systems are often interfered by background noise, such as voice from other people or devices, collected by the preceding automatic speech recognition (ASR) module. Therefore, the chit-chat ability may also improve the robustness of a task-oriented dialog system (Zhao et al., 2017) .", | |
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| "text": "As shown in Table1, there are many differences between chit-chat and task-oriented dialogues. Creating a single model for different tasks without performance degradation is challenging (Kaiser et al., 2017) . Some works attempt to model different dialogue skills via different experts or adapters (Madotto et al., 2020; Lin et al., 2021) . However, these methods increase the number of parameters and hard to achieve satisfactory performance on both types of dialogues. Besides, previous work lack the exploration of the ability to switch between different types of dialogues.", | |
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| "section": "System", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This work proposes a auto-regressive language model based dialogue system to handle chit-chat", | |
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| "text": "Turns Mainstream method Chit-chat Strong Entertainment Long End-to-end method Task-oriented dialogue Weak Completing tasks Short Pipeline method * Table 1 : Differences between chit-chat and task-oriented dialogues. *: The model will predict belief state and system act before giving a response, to this end, the training set needs to be annotated with belief state and system act.", | |
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| "section": "Diversity Purpose", | |
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| "text": "and TOD in a unified framework (UniDS). Specifically, since chit-chat data do not have explicit belief state and agent action, to unify chit-chat and task-oriented dialogues format, we device belief state and agent act for chit-chat dialogues as taskoriented dialogues. On the other hand, because of the diversity of chit-chat, chit-chat dialogue systems need more training data than task-oriented dialogue systems, e.g., 147,116,725 dialogues for DialoGPT (Radford et al., 2019) and 8,438 dialogues for UBAR (Yang et al., 2021) . To overcome this difference, we propose to train UniDS in a twostage way. A chit-chat model is first trained with huge chit-chat dialogues, and then we train UniDS from the chit-chat dialogue system with mixed dialogues based on our proposed unified dialogue data schema.", | |
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| "text": "We evaluate UniDS using a public task-oriented dialogue dataset MultiWOZ and a chit-chat dataset extracted from Reddit 1 through both automatic and human evaluations. UniDS achieves comparable performance compared to the state-of-the-art chit-chat dialogue system DialoGPT, and TOD system UBAR. In addition, we empirically show that UniDS is more robust to noise in task-oriented dialogues, and UniDS shows a desirable ability to switch between the two types of dialogues.", | |
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| "text": "The contributions of this work are summarised as follows:", | |
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| "section": "Diversity Purpose", | |
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| "text": "\u2022 To the best of our knowledge, this is the first work presenting a unified dialogue system to jointly handle chit-chat and task-oriented dialogues in an end-to-end way.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Diversity Purpose", | |
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| { | |
| "text": "\u2022 We design a unified dialogue data schema for chit-chat and TOD, allowing the training and inference of dialogue systems to be performed in a unified manner.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Diversity Purpose", | |
| "sec_num": null | |
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| "text": "\u2022 To tackle the gap between chit-chat dialogue systems and task-oriented dialogue systems in the requirement of training data, a two-stage training method is proposed to train UniDS.", | |
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| "text": "\u2022 Extensive empirical results show that UniDS performs comparably to state-of-the-art chitchat dialogue systems and task-oriented dialogue systems. Moreover, UniDS achieves better robustness to dialog noise and satisfactory switch ability between two types of dialogues.", | |
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| "text": "With the development of large-scale language models, chit-chat dialogue systems achieve remarkable success. Based on GPT-2 (Radford et al., 2019) , DialoGPT (Zhang et al., 2020) is further trained on large-scale dialogues extracted from Reddit. Di-aloGPT could generate more relevant, contentful, and fluent responses than previous methods. Afterwards, larger pre-train LM based chit-chat dialogue systems (Adiwardana et al., 2020; Bao et al., 2020; are proposed and achieve even better performance. In the area of task-oriented dialogue systems, recent research (Hosseini-Asl et al., 2020; Peng et al., 2020; Yang et al., 2021) concatenated elements in a dialogue into one sequence and utilized pre-train LM to generate the belief state, system act, and response in an end-to-end way and achieved promising results. There are several works related to the unified dialogue system. Zhao et al. (2017) insert one turn chit-chat dialogue into task-oriented dialogues to train a model with better out-of-domain recovery ability. Attention over Parameters (AoP) (Madotto et al., 2020) utilizes different decoders for different dialogue skills (e.g., hotel booking, restaurant booking, chit). However, the performance of AoP can be improved and it largely increases parameters comparing with models that handle a single type of dialogues. ACCENTOR (Sun et al., 2021) adds chit-chat utterance at the beginning or end of task-oriented responses to make the conversation more engaging, but ACCENTOR is unable to have a chit-chat with users. Unlike the above works, UniDS does not add extra parameters to existing dialogue models, and UniDS could alternatively handle chit-chat and task-oriented dialogues in a seamless way.", | |
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| "text": "As illustrated in Figure 2 , we formulate unified dialogue system as an auto-regressive language model. A dialogue session at turn t has the following components: user input U t , belief state B t , database search result D t , system act A t , and response R t . Each component consists of tokens from a fixed vocabulary. For turn t, the dialogue context C t is the concatenation of all the components of the previous dialogues as well as the user input at turn t:", | |
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| "sec_num": "3.1" | |
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| { | |
| "text": "C t = [U 0 , B 0 , D 0 , A 0 , R 0 , \u2022 \u2022 \u2022 , R t\u22121 , U t ].", | |
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| "text": "Given the dialogue context C t , UniDS first generates the belief state B t :", | |
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| { | |
| "text": "EQUATION", | |
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| "raw_str": "B t = UniDS(C t ) ,", | |
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| "text": "and use it to search the database to get the search result D t . Then, UniDS generates the system act A t conditioned on the updated context by extending C t with B t and D t :", | |
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| "raw_str": "A t = UniDS([C t , B t , D t ]) .", | |
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| "text": "Lastly, the response R t is generated conditioned on the concatenation of all previous components:", | |
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| "raw_str": "R t = UniDS([C t , B t , D t , A t ]) .", | |
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| "text": "In the widely adopted task-oriented dialogue system pipeline, a dialogue session consists of a user input utterance, a belief state that represents the user intention, a database search result, a system act, and a system response (Young et al., 2013; Yang et al., 2021) . However, due to the diversity of chit-chat and the cost of manual annotation, chit-chat dialogue systems do not assume the existence of the belief state nor system act (Bao et al., 2020; Zhang et al., 2020) . The inconsistency of data format between chit-chat and TOD hinders the implementation of a unified model. To tackle this problem, we design a data schema with belief state, database result representation and system act for chit-chat. Table 2 illustrates such unified data schema with examples. The following sections explain each component in detail.", | |
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| "text": "The unified belief state is represented in the form of \"<domain> slot [value]\". A belief state could have several domains, each containing several slot-value pairs. As we can observe, extracting belief state of TOD may need to copy some words from the user utterance. To make UniDS keep this copy mechanism, for chit-chat, nouns in the user utterance U t are extracted as the slot or value of belief state.", | |
| "cite_spans": [], | |
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| "section": "Belief state", | |
| "sec_num": "3.2.1" | |
| }, | |
| { | |
| "text": "We use a special token to represent the number of matched entities under the constraints of the belief state in the current turn.", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "DB result", | |
| "sec_num": "3.2.2" | |
| }, | |
| { | |
| "text": "System acts are represented as \"<domain> <act> [slot]\" for TOD. The meaning of \"<domain>\" is the same as in belief states. \"[act]\" denotes the type of action the system needs to perform. Following the \"domain-act\" pair, slots are optional. For chit-chat, token \"<chit_act>\" denotes the dialogue system will chat with the user. Therefore, a processed dialogue sequence X t at turn t for either TOD or chit-chat can be both represented as:", | |
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| "section": "System act", | |
| "sec_num": "3.2.3" | |
| }, | |
| { | |
| "text": "X t = [C t , B t , D t , A t , R t ].", | |
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| "section": "System act", | |
| "sec_num": "3.2.3" | |
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| { | |
| "text": "(4)", | |
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| "text": "Since the diversity of chit-chat in topics and terms, chit-chat dialogue systems need much larger training data than task-oriented dialogue systems. If directly training UniDS with the unified dialogue data which contains much more chit-chat dialogues than task-oriented dialogues, the trained model may ignore the ability to complete task-oriented dialogues. Therefore, this work proposes a two-stage method for training UniDS. As illustrated in Figure 3 , we propose to first train a chit-chat dialogue model with huge chit-chat dialogues, and then we train UniDS from the chit-chat dialogue system with mixed dialogues. The mixed dialogue data is obtained by mixing chit-chat and TOD data which are pre-processed by the proposed unified data schema in the ratio of 1:1. Motivated by the recent success of applying GPT-2 for task-oriented dialogue systems (Hosseini-Asl et al., 2020; Peng et al., 2020; Yang et al., 2021) and chit-chat dialogue systems (Zhang et al., 2020) , we use DialoGPT (Zhang et al., 2020) in an auto-regressive manner as:", | |
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| "text": "Figure 3", | |
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| "section": "Two-stage training method", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "EQUATION", | |
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| "start": 0, | |
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| "raw_str": "L = N i=1 \u2212 log P (x i |x <i ) ,", | |
| "eq_num": "(5)" | |
| } | |
| ], | |
| "section": "Two-stage training method", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "where x i is a token of X t , and x <i are the preceding tokens. tiWOZ have 8438/1000/1000 dialogues, respectively. Each dialogue contains 1 to 3 domains.", | |
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| "section": "Two-stage training method", | |
| "sec_num": "3.3" | |
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| "text": "We derived open-domain chit-chat dialogue from Reddit dump 3 . To avoid overlapping, the chit-chat training set and test set are extracted from the Reddit posts in 2017 and 2018 respectively. To ensure the generation quality, we conduct a careful data cleaning. A conversation will be filtered when (1) there is a URL in the utterance;", | |
| "cite_spans": [], | |
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| "section": "Chit-chat Dataset", | |
| "sec_num": "4.1.2" | |
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| { | |
| "text": "(2) there is an utterance longer than 200 words or less than 2 words; (3) the dialogue contains \"[removed]\" or \"[deleted]\" tokens; (4) the number of utterances in the dialogue is less than 4; (5) the dialogue contains offensive words. Finally, we sample 8, 438 dialogues for training which is the same size as the training set of MultiWOZ. The validation set and test set contain 6, 000 dialogues and 8, 320 dialogues, respectively.", | |
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| "section": "Chit-chat Dataset", | |
| "sec_num": "4.1.2" | |
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| { | |
| "text": "For chit-chat dialogue, we compare UniDS with Di-aloGPT (Zhang et al., 2020) . For fair comparisons, we further fine-tune a 12-layer DialoGPT and a 24layer DialoGPT with our chit-chat dialogue training set, which we refer to as DialoGPT-12L and DialoGPT-24L, respectively. For TOD, we consider the state-of-the-art endto-end TOD system UBAR (Yang et al., 2021) and PPTOD (Su et al., 2021) . For a fair comparison with UniDS, we also fine-tune UBAR from 12 layers DialoGPT and 24 layers DialoGPT with Multi-WOZ dataset, the fine-tuned models are denoted as UBAR-12L and UBAR-24L, respectively.", | |
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| "section": "Baselines", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "UniDS and other baselines are implemented based on HuggingFace's Transformers (Wolf et al., 2019) . The max sequence length is 1024 and sequences longer than 1024 are truncated from the head. We use the AdamW optimizer (Loshchilov and Hutter, 2019 ) and greedy decoding method for inference. All models are trained on a single Tesla V100, and we perform a hyper-parameter search on batch size and learning rate. The best model and hyperparameter are selected through the performance on the validation set of MultiWOZ only.", | |
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| "text": "(Wolf et al., 2019)", | |
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| "text": "(Loshchilov and Hutter, 2019", | |
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| "section": "Implementation Details", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "As shown in Table1, chit-chat dialogues need to attract users to talk more, while TOD needs to complete tasks as soon as possible. Therefore, a model trained with the mixed dialogue data tends to talk long turns instead of efficiently completing the task. Since entity recommendation acts are important for dialogue system to complete tasks efficiently, we use a weighted cross-entropy loss as the training objective of UniDS. We assign larger weights to tokens about entity recommendation actions. We empirically set the weight of entity recommendation actions in loss function to 2 4 , weights of other 4 The appendix gives discussions for other values of weight, but does not affect the overall conclusion. tokens are set to 1 by default.", | |
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| "section": "Implementation Details", | |
| "sec_num": "4.3" | |
| }, | |
| { | |
| "text": "For chit-chat dialogues, the BLEU score (Papineni et al., 2002) and the average length of the generated responses are reported. Because of the diversity of chit-chat, BLEU may be difficult to reflect the quality of chit-chat responses, we also report distinct-1 and distinct-2 (Li et al., 2016) of generated dialogues, which is defined as the rate of distinct uniand bi-grams in the generated sentences. We also conduct a human evaluation on 50 randomly sampled test dialogues for two 24 layers models. Three judges evaluate them in terms of relevance, informativeness, and how human-like the response is with a 3-point Likert-like scale (Joshi et al., 2015) .", | |
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| "text": "(Li et al., 2016)", | |
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| "section": "Evaluation Metrics", | |
| "sec_num": "4.4" | |
| }, | |
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| "text": "For TOD, we follow UBAR to use the following automatic metrics: Inform refers to the rate of the entities provided by a model are correct; success measures the rate of a model has answered all the requested information; and BLEU to measure the fluency of generated responses. A combined score is computed as (Inform + Success) \u00d7 0.5 + BLEU to measure the overall response quality. chat ability even after training with the mixed dialogue data.", | |
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| "text": "ii) For the TOD task, UniDS achieves better performance than UBAR for the same parameter size. For both 12L and 24L DialoGPT, UniDS improves the BLEU score and the Combined score compared with UBAR. We believe this is because combining chit-chat dialogues for training helps the model to generate more fluent responses.", | |
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| "section": "Overall results", | |
| "sec_num": "4.5" | |
| }, | |
| { | |
| "text": "Furthermore, we also provide the human evaluation results in Table 5 . UniDS is compared to DialoGPT regarding three dimensions for chit-chat dialogues.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
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| "start": 61, | |
| "end": 68, | |
| "text": "Table 5", | |
| "ref_id": "TABREF7" | |
| } | |
| ], | |
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| "section": "Overall results", | |
| "sec_num": "4.5" | |
| }, | |
| { | |
| "text": "We could see that UniDS consistently wins the majority cases for all three aspects, including relevance, informativeness, and human-like.", | |
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| "section": "Overall results", | |
| "sec_num": "4.5" | |
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| { | |
| "text": "In this experiment (c.f. Table 4 ), we compare two simplified versions of UniDS to understand the effects of different components. For comparison, we report the performance of 1) removing slots in belief state of chit-chat, denoted as \"UniDS w/o chit-chat BS\", and 2) replacing the weighted crossentropy loss with a standard cross-entropy loss, denoted as \"UniDS w/o weighted loss\". Next, we elaborate our observations w.r.t. these two components. w/o chit-chat BS: When removing the belief state of chit-chat dialogues, the performances of both UniDS-12L and UniDS-24L drop w.r.t. inform, success, and combined score for TOD. We believe the reason is that the process of extracting the belief state needs to copy some keywords from the user utterance, and even extracting nouns as belief state for chit-chat is helpful for UniDS to learn this copy mechanism in the TOD task. Taking the case in Figure 4 as an example, UniDS w/o chit-chat BS (left) fails to extract the user's interest in searching restaurants, while UniDS (right) extracts the restaurant slot successfully. As a result, UniDS could recommend the right entities. Furthermore, removing chit-chat BS does not degrade the performance of chit-chat. w/o weighted loss: When replacing the weighted cross-entropy loss in UniDS with standard crossentropy loss, we observe a notable drop w.r.t. inform, success, and combined score in task-oriented metrics. These results demonstrate that giving more attention to entity recommendation acts helps the task completion capability. Moreover, dropping the weight loss does not affect the performance of chitchat much.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 25, | |
| "end": 32, | |
| "text": "Table 4", | |
| "ref_id": "TABREF6" | |
| }, | |
| { | |
| "start": 895, | |
| "end": 903, | |
| "text": "Figure 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Ablation Study", | |
| "sec_num": "4.6.1" | |
| }, | |
| { | |
| "text": "Overall, we contend both \"chit-chat BS\" and \"weighted loss\" are beneficial for task-oriented dialogues without degrading the chit-chat capability.", | |
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| "section": "Ablation Study", | |
| "sec_num": "4.6.1" | |
| }, | |
| { | |
| "text": "In real-world scenarios, it is common and natural for users to switch between chit-chat and taskoriented dialogues. Therefore, we investigate the switch ability of UniDS in this subsection. To simulate the scenario of dialogue switching, we consider two setups: (1) having two turns of chit-chat dialogues before the start of a task-oriented dialogue and (2) pre-pending two turns of task-oriented dialogues at the beginning of a chit-chat dialogue. To evaluate the model's ability to switch between two types of dialogues, we propose a metric, called Switch-n, which is defined as the rate of a model switches its response type within the first n turns after a user switches the type of input. Additionally, we also report the model performance after the switching.", | |
| "cite_spans": [], | |
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| "section": "Analysis of Switching Ability", | |
| "sec_num": "4.6.2" | |
| }, | |
| { | |
| "text": "Tables 6 and 7 present the results of the two switching setups, and we have the following observations:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of Switching Ability", | |
| "sec_num": "4.6.2" | |
| }, | |
| { | |
| "text": "(i) It is not surprising that adding switching tasks for both chit-chat and TOD degrades the performance of UniDS, as the added 2 turns of switching utterances introduce irrelevant con- tent, which distracts the model. However, focusing on the switching task, we observe that for almost 98% of cases, UniDS can success in dialogue task switching, from chit-chat to TOD and vice versa, within the first two turns (Switch-1 and Switch-2). This demonstrates UniDS has a good ability to switch between two types of dialogue tasks.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis of Switching Ability", | |
| "sec_num": "4.6.2" | |
| }, | |
| { | |
| "text": "(ii) When switching from task-oriented dialogues to chit-chat dialogues, the value of Switch-1 is relatively low, this may because our model tends to confirm user intents or give a transitional response rather than switch to chit-chat mode immediately. As the case shown in Table 8, when the user switches from TOD to chit-chat, UniDS gives a chatty response and thanks the user for using its services.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Analysis of Switching Ability", | |
| "sec_num": "4.6.2" | |
| }, | |
| { | |
| "text": "Many real-world dialogue systems need real-time speech recognition to interact with users, which is easily interfered by background noise from the background environment (e.g. other people and devices). Therefore, we analyze the robustness of UniDS and UBAR by inserting several turns of Figure 5 : Examples of UBAR-DialoGPT-24L and UniDS-24L when inserting a task-irrelevant utterance in a task-oriented dialogue. UBAR-DialoGPT reserves a train for the user randomly, which makes the task failed because the user intent is incomplete; while UniDS keeps the previous belief state and gives a chatty response. When the user returns to the TOD, UniDS could continue with the task.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 288, | |
| "end": 296, | |
| "text": "Figure 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Robustness Study", | |
| "sec_num": "4.6.3" | |
| }, | |
| { | |
| "text": "irrelevant chit-chat utterances into the TOD, and we evaluate the model performance against such noise.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Robustness Study", | |
| "sec_num": "4.6.3" | |
| }, | |
| { | |
| "text": "As observed in Table 9 , both UniDS and UBAR drops on the combined score when only one turn of chit-chat dialogue is inserted. However, UniDS drop less than UBAR (about 4 vs. 6 points). Similarly, when two turns of chit-chat are inserted into TOD, UniDS drops about 8 points, and UBAR drops about 11 points on the combined score. These results demonstrate that UniDS has stronger robustness to such task-irrelevant noise than UBAR. We present an interesting case in Figure 5 . When giving a task-irrelevant utterance, UBAR-24L reserves a train for the user randomly, which makes the task failed because the user intent is incomplete, while UniDS keeps the previous belief state and gives a chatty response. When the user returns to the TOD, UniDS can continue with the task.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 15, | |
| "end": 22, | |
| "text": "Table 9", | |
| "ref_id": "TABREF13" | |
| }, | |
| { | |
| "start": 466, | |
| "end": 474, | |
| "text": "Figure 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Robustness Study", | |
| "sec_num": "4.6.3" | |
| }, | |
| { | |
| "text": "This paper proposes a unified dialogue system (UniDS) to jointly handle both chit-chat and taskoriented dialogues in an end-to-end framework. Specifically, we propose a unified dialogue data schema for both chit-chat and task-oriented dialogues, and a two-stage method to train UniDS. To our best knowledge, this is the first study towards an end-to-end unified dialogue system.", | |
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| "section": "Conclusion", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Experiments show that UniDS performs comparably with state-of-the-art chit-chat dialogue systems and task-oriented dialogue systems without adding extra parameters to current chit-chat dialogue systems. More importantly, the proposed UniDS achieves good switch ability and shows better robustness than pure task-oriented dialogue systems. Although question answering (QA) is not considered in the proposed UniDS, as an initial attempt, our explorations may inspire future studies towards building a general dialogue system.", | |
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| "section": "Conclusion", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We notice that some chit-chat utterances generated by the proposed UniDS may be unethical, biased or offensive. Toxic output is one of the main issues of current state-of-the-art dialogue models trained on large naturally-occurring datasets. We look forward to furthering progress in the detection and control of toxic outputs.", | |
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| "section": "Ethical Considerations", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "https://www.reddit.com/", | |
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| "text": "https://files.pushshift.io/reddit/comments/", | |
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| "title": "Towards a human-like opendomain chatbot", | |
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| "first": "Daniel", | |
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| "first": "Minh-Thang", | |
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| "FIGREF1": { | |
| "type_str": "figure", | |
| "num": null, | |
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| "text": "Illustration of users being interested to chitchat with the dialogue system before booking a hotel." | |
| }, | |
| "FIGREF2": { | |
| "type_str": "figure", | |
| "num": null, | |
| "uris": null, | |
| "text": "Training process of UniDS." | |
| }, | |
| "TABREF0": { | |
| "text": "as our chit-chat model, and train UniDS from DialoGPT.The training objective for UniDS is to maximize the joint probability of all tokens in X t computed does ... buy happiness? I am ... cheap hotel.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>Chit-chat</td><td/><td/><td/><td/></tr><tr><td>Task-oriented</td><td>Belief state generation</td><td colspan=\"2\">System act generation</td><td colspan=\"2\">Response generation</td></tr><tr><td>UniDS</td><td/><td/><td/><td/></tr><tr><td/><td>[chit] money happiness</td><td>[db_nore]</td><td colspan=\"2\">[chit] [chit_act]</td><td>depends on ... on it .</td></tr><tr><td/><td>[hotel] price cheap</td><td>[db_2]</td><td colspan=\"2\">[hotel] ... area</td><td>okey , do you ... stay in ?</td></tr><tr><td>Dialog history</td><td>Belief state</td><td>DB result</td><td colspan=\"2\">System act</td><td>Response</td></tr><tr><td/><td colspan=\"3\">Figure 2: The architecture of UniDS.</td><td/></tr><tr><td colspan=\"4\">Unified dialogue data schema Chit-chat example Tokenized utterance does money buy happiness ? Belief state <domain> slot [value] User input <chit> money happiness DB result A token indicated the number <db_nore> of candidate entities Act <domain> <act> [slot] <chit> <chit_act> Response Tokenized utterance depends on how much money you spend on it .</td><td colspan=\"2\">Task-oriented example i am looking for a cheap hotel . <hotel> price cheap <db_2> <hotel> <request> area do you have a specific area you want to stay in ?</td></tr></table>", | |
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| }, | |
| "TABREF1": { | |
| "text": "Unified dialogue data schema (where tokens inside the square bracket are optional) and examples.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td/><td>Trained with</td><td/></tr><tr><td>Chit-chat dialogue model</td><td>mixed dialogues</td><td>UniDS</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF4": { | |
| "text": "Automatic evaluations of UniDS with two model sizes over two types of dialogue datasets. All results are reported in percentage, except Combined and AvgLen. Best results are in bold. *: Results reported in original paper(Yang et al., 2021) is not obtained by end-to-end evaluation. This result is reported by authors of UBAR in https://github.com/TonyNemo/UBAR-MultiWOZ/issues/3.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table/>", | |
| "html": null | |
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| "TABREF5": { | |
| "text": "", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>presents the overall comparison results of</td></tr><tr><td>automatic evaluation. The first block shows the</td></tr><tr><td>results of UBAR. The following two blocks are</td></tr><tr><td>various baselines trained on 12 or 24 layers Di-</td></tr><tr><td>aloGPT respectively. From these results, we have</td></tr><tr><td>the following observations.</td></tr><tr><td>i) For the chit-chat task, UniDS achieves com-</td></tr><tr><td>parable performance with DialoGPT. For the</td></tr><tr><td>BLEU score, UniDS outperforms DialoGPT</td></tr><tr><td>with 12L and 24L. On other metrics, UniDS</td></tr><tr><td>is comparable with DialoGPT. This demon-</td></tr><tr><td>strates that UniDS can still keep strong chit-</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF6": { | |
| "text": "Ablation studies of automatic evaluations for UniDS.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td/><td>...</td><td colspan=\"2\">UniDS-24L w/o chit-chat BS</td><td>...</td><td>UniDS-24L</td></tr><tr><td/><td colspan=\"2\">Sure, give me their phone number. I</td><td/><td>Sure, give me their phone number. I</td></tr><tr><td/><td colspan=\"2\">would also like to find an expensive</td><td/><td>would also like to find an expensive</td></tr><tr><td>User</td><td colspan=\"2\">restaurant in west cambridge</td><td>User</td><td>restaurant in west cambridge</td></tr><tr><td/><td>DB</td><td colspan=\"2\">Act: [attraction] [inform] phone name address Belief state: [attraction] area west</td><td>DB</td><td>Act: [train] [request] destination Belief state:[attraction] area west [restaurant] pricerange expensive area west</td></tr><tr><td/><td/><td>[value_name] is located at [value_address]</td><td/><td>Here's the number for the [value_name], [value_phone].</td></tr><tr><td/><td/><td>and their phone number is [value_phone].</td><td>System</td><td>How does the [value_name] sound for you?</td><td>System</td></tr><tr><td colspan=\"5\">Figure 4: TOD examples from UniDS w/o chit-chat BS and UniDS. UniDS w/o chit-chat BS does not extract the user intent of searching restaurants, but UniDS extracts this intent successfully (highlighted in italics).</td></tr><tr><td colspan=\"2\">Relevance Informativeness Human-like</td><td colspan=\"2\">DialoGPT-24L Neutral UniDS-24L (Win %) (% ) (Win %) 25.33 42.67 32.00 29.33 33.33 37.34 26.67 43.33 30.00</td></tr></table>", | |
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| "TABREF7": { | |
| "text": "", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>: Win rate [%] between the UniDS-24L and DialoGPT-24L using three human evaluation metrics on chit-chat dialogues. \"Neutral\" means the generated responses of DialoGPT-24L and UniDS-24L are consid-ered to have equal quality.</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF9": { | |
| "text": "Switching performance of UniDS when having 2 turns chit-chat dialogues before task-orientated dialogues. Numbers in brackets indicates the exactly switching rate at the 2nd turn.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td colspan=\"5\">UniDS BLEU Dist-1 Dist-2 AvgLen Switch-1 Switch-2</td></tr><tr><td>12L 0.22 24L 0.34</td><td>4 6</td><td>19 31</td><td>14.15 16.18</td><td>31.8 98.9 (+67.1) 37.0 96.6 (+59.6)</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF10": { | |
| "text": "", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>: Switching performance of UniDS when pre-pending 2 turns task-oriented dialogues before chit-chat.</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF12": { | |
| "text": "", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>: Example of UniDS when switching from the task-oriented dialogue to chit-chat. UniDS gives a chatty response and thanks the user for using its services. Dia-logue history is omitted.</td></tr><tr><td>Model UBAR-12L 99.18 93.76 (-5.42) 88.14 (-11.04) Base 1 turn 2 turns UniDS-12L 100.06 96.13 (-3.93) 91.42 (-8.64) UBAR-24L 99.31 93.08 (-6.23) 88.67 (-10.64) UniDS-24L 104.12 100.71 (-3.41) 95.68 (-8.44)</td></tr></table>", | |
| "html": null | |
| }, | |
| "TABREF13": { | |
| "text": "Combined score over TOD dataset for robustness test by inserting 1 and 2 turns of task-irrelavant utterances. Full results are presented in Appendix.", | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table/>", | |
| "html": null | |
| } | |
| } | |
| } | |
| } |