File size: 2,032 Bytes
66675d3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
378d4ad
66675d3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
---
language:
- ar 
- az 
- be 
- bg 
- bn 
- bs 
- cs 
- da 
- de 
- el 
- en 
- eo 
- es 
- et 
- eu 
- fa 
- fi 
- fr 
- gl 
- he 
- hi 
- hr 
- hu 
- hy 
- id 
- it 
- ja 
- ka 
- kk 
- ko 
- ku 
- lt 
- mk 
- mn 
- mr 
- ms 
- my 
- nb 
- nl 
- pl 
- pt 
- ro 
- ru 
- sk 
- sl 
- sq 
- sr 
- sv 
- ta 
- th 
- tr 
- uk 
- ur 
- vi 
- zh 
language_creators:
- expert-generated
annotations_creators:
- crowdsourced
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
pretty_name: TED_Talks
task_categories:
  - translation
---
## Dataset Description

Train, validation and test splits for TED talks as in http://phontron.com/data/ted_talks.tar.gz. Data is detokenized using moses. 

Example of loading:
```python
dataset = load_dataset("davidstap/ted_talks", "ar_en", trust_remote_code=True)
```

Note that `ar_en` and `en_ar` will result in the same data being loaded..


The following languages are available:
```
- ar 
- az 
- be 
- bg 
- bn 
- bs 
- cs 
- da 
- de 
- el 
- en 
- eo 
- es 
- et 
- eu 
- fa 
- fi 
- fr 
- fr-ca 
- gl 
- he 
- hi 
- hr 
- hu 
- hy 
- id 
- it 
- ja 
- ka 
- kk 
- ko 
- ku 
- lt 
- mk 
- mn 
- mr 
- ms 
- my 
- nb 
- nl 
- pl 
- pt 
- pt-br 
- ro 
- ru 
- sk 
- sl 
- sq 
- sr 
- sv 
- ta 
- th 
- tr 
- uk 
- ur 
- vi 
- zh 
- zh-cn 
- zh-tw
```


### Citation Information

```
@inproceedings{qi-etal-2018-pre,
    title = "When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?",
    author = "Qi, Ye  and
      Sachan, Devendra  and
      Felix, Matthieu  and
      Padmanabhan, Sarguna  and
      Neubig, Graham",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-2084",
    doi = "10.18653/v1/N18-2084",
    pages = "529--535",
}
```