Papers by Ryan Culkin
Multi-Sentence Argument Linking (2020.acl-main)
Copied to clipboard
| Challenge: | Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking. |
| Approach: | They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution. |
| Outcome: | The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
Copied to clipboard
J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Neural-Davidsonian Semantic Proto-role Labeling (D18-1)
Copied to clipboard
| Challenge: | Existing models for semantic proto-role labeling are based on a bidirectional LSTM encoding strategy. |
| Approach: | They propose a neural model for semantic proto-role labeling using a bidirectional LSTM encoding strategy that is adapted for the task. |
| Outcome: | The proposed model achieves state-of-the-art in a sentence with a LSTM encoder and a decoder. |
Iterative Paraphrastic Augmentation with Discriminative Span Alignment (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing datasets can be expanded or created using a small, manually produced seed corpus. |
| Approach: | They propose a paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrases and discriminative span alignment. |
| Outcome: | The proposed approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. |