CFS3

The Communicative-Function-labeled Semantic Scholar Sentence Dataset (CFS3) is a distantly-labeled dataset that includes 100,016 sentences with 77 communicative-function labels.
All sentences are derived from S2ORC.

If you find this useful, please cite our work.

citation

@inproceedings{sugimoto-aizawa-2022-incorporating,
    title = "Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric Learning",
    author = "Sugimoto, Kaito  and
      Aizawa, Akiko",
    booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.sdp-1.7",
    pages = "54--68",
    abstract = "Communicative functions are an important rhetorical feature of scientific writing. Sentence embeddings that contain such features are highly valuable for the argumentative analysis of scientific documents, with applications in document alignment, recommendation, and academic writing assistance. Moreover, embeddings can provide a possible solution to the open-set problem, where models need to generalize to new communicative functions unseen at training time. However, existing sentence representation models are not suited for detecting functional similarity since they only consider lexical or semantic similarities. To remedy this, we propose a combined approach of distant supervision and metric learning to make a representation model more aware of the functional part of a sentence. We first leverage an existing academic phrase database to label sentences automatically with their functions. Then, we train an embedding model to capture similarities and dissimilarities from a rhetorical perspective. The experimental results demonstrate that the embeddings obtained from our model are more advantageous than existing models when retrieving functionally similar sentences. We also provide an extensive analysis of the performance differences between five metric learning objectives, revealing that traditional methods (e.g., softmax cross-entropy loss and triplet loss) outperform state-of-the-art techniques.",
}

---

Each label number corresponds to a total of 80 different functional classes included in one of the links below (accessed Nov. 2021).

• https://www.phrasebank.manchester.ac.uk/introducing-work/ (label: 01 ~ 17)
• https://www.phrasebank.manchester.ac.uk/referring-to-sources/ (label: 18 ~ 32)
• https://www.phrasebank.manchester.ac.uk/describing-methods/ (label: 33 ~ 46)
• https://www.phrasebank.manchester.ac.uk/reporting-results/ (label: 47 ~ 60)
• https://www.phrasebank.manchester.ac.uk/discussing-findings/ (label: 61 ~ 72)
• https://www.phrasebank.manchester.ac.uk/writing-conclusions/ (label: 73 ~ 80)

Label Distribution

Labels 15, 29, and 39 are assigned to no sentence.

label	CFS3	CFS3_train	CFS3_valid
01	5212	4170	1042
02	520	416	104
03	334	267	67
04	5165	4132	1033
05	726	581	145
06	283	226	57
07	140	112	28
08	541	433	108
09	981	785	196
10	4101	3281	820
11	1735	1388	347
12	1339	1071	268
13	300	240	60
14	1102	882	220
15	0	0	0
16	142	114	28
17	493	394	99
18	73	58	15
19	134	107	27
20	172	138	34
21	211	169	42
22	10686	8549	2137
23	10	8	2
24	59	47	12
25	184	147	37
26	107	86	21
27	4	3	1
28	68	54	14
29	0	0	0
30	245	196	49
31	2	2	0
32	526	421	105
33	1694	1355	339
34	4394	3515	879
35	340	272	68
36	2950	2360	590
37	1774	1419	355
38	823	658	165
39	0	0	0
40	426	341	85
41	257	206	51
42	69	55	14
43	71	57	14
44	160	128	32
45	1988	1590	398
46	49	39	10
47	699	559	140
48	11873	9498	2375
49	2404	1923	481
50	1033	826	207
51	1642	1314	328
52	9	7	2
53	203	162	41
54	139	111	28
55	66	53	13
56	12	10	2
57	42	34	8
58	6	5	1
59	91	73	18
60	416	333	83
61	776	621	155
62	114	91	23
63	3096	2477	619
64	543	434	109
65	5463	4370	1093
66	982	786	196
67	3800	3040	760
68	1254	1003	251
69	746	597	149
70	121	97	24
71	23	18	5
72	628	502	126
73	3243	2594	649
74	447	358	89
75	1460	1168	292
76	2805	2244	561
77	2091	1673	418
78	169	135	34
79	2466	1973	493
80	564	451	113

License

CC BY-NC 4.0