| [ | |
| { | |
| "file": "paper_22.txt", | |
| "start": 169, | |
| "end": 179, | |
| "label": "Unsupported claim", | |
| "user": "Ed", | |
| "text": " STS tasks" | |
| }, | |
| { | |
| "file": "paper_22.txt", | |
| "start": 635, | |
| "end": 964, | |
| "label": "Coherence", | |
| "user": "Ed", | |
| "text": "Specifically, Reimers and Gurevych (2019) mainly use the classification objective for an NLI dataset, and Wu et al. (2020) adopt contrastive learning to utilize self-supervision from a large corpus. Yan et al. (2021); Gao et al. (2021) incorporate a parallel corpus such as NLI datasets into their contrastive learning framework." | |
| }, | |
| { | |
| "file": "paper_22.txt", | |
| "start": 1101, | |
| "end": 1201, | |
| "label": "Unsupported claim", | |
| "user": "Ed", | |
| "text": "One related task is interpretable STS, which aims to predict chunk alignment between two sentences ." | |
| }, | |
| { | |
| "file": "paper_22.txt", | |
| "start": 1291, | |
| "end": 1313, | |
| "label": "Format", | |
| "user": "Ed", | |
| "text": "(Konopík et al., 2016;" | |
| }, | |
| { | |
| "file": "paper_22.txt", | |
| "start": 1863, | |
| "end": 1881, | |
| "label": "Format", | |
| "user": "Ed", | |
| "text": " (Li et al., 2020;" | |
| }, | |
| { | |
| "file": "paper_22.txt", | |
| "start": 2408, | |
| "end": 2746, | |
| "label": "Coherence", | |
| "user": "Ed", | |
| "text": ". To get the solution efficiently, Cuturi (2013) provides a regularizer inspired by a probabilistic theory and then uses Sinkhorn's algorithm. Kusner et al. (2015) relax the problem to get the quadratic-time solution by removing one of the constraints, and Wu et al. (2018) introduce a kernel method to approximate the optimal transport.\n" | |
| } | |
| ] |