Papers with supervised learning approaches
AutoReply: Detecting Nonsense in Dialogue with Discriminative Replies (2023.findings-emnlp)
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| Challenge: | Existing models for dialogue detection make many errors in their own messages . a dataset of long dialogues richly grounded in the game state contains many errors . |
| Approach: | They propose to use an annotated dialogue dataset to generate automatic responses for dialogue models. |
| Outcome: | The proposed model outperforms handcrafted replies and performs on par with supervised learning approaches. |
Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems (N19-2)
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| Challenge: | Existing datasets for grammatical error correction don’t capture the distribution of errors that data-driven generators are likely to make. |
| Approach: | They propose a framework that allows candidates to be filtered and ranked to select the best response. |
| Outcome: | The proposed framework can be scaled with relatively low effort and achieve high precision with reasonable recall on a weather domain dataset. |
Data Programming for Learning Discourse Structure (P19-1)
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| Challenge: | Discourse structures are a relational semantic structure that convey causal, topical, argumentative relations or more generally coherence relations. |
| Approach: | They propose to use Snorkel to label training data using expert-composed heuristics and transform them into probability distributions of the class labels given to training candidates. |
| Outcome: | The proposed paradigm can be used for difficult tasks such as that of discourse attachment. |
Split or Merge: Which is Better for Unsupervised RST Parsing? (D19-1)
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| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |