Papers by Sam Tang
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations (N19-1)
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| Challenge: | Empirically, the model with the best performing syntactic and semantic representations gives rise to the most disentangled representations. |
| Approach: | They propose a generative model that uses latent variables to learn a sentence that uses both latent and latent representations. |
| Outcome: | The proposed model achieves better disentanglement between semantic and syntactic representations by training with multiple losses, including losses that exploit aligned paraphrastic sentences and word-order information. |
Document-level Entity-based Extraction as Template Generation (2021.emnlp-main)
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| Challenge: | Document-level entity-based extraction (EE) tasks extract entity-centric information from unstructured text across multiple sentences. |
| Approach: | They propose a generative framework for two document-level EE tasks: role-filler entity extraction (RE) and relation extraction ( RE). |
| Outcome: | The proposed framework captures cross-entity dependencies and avoids exponential computation complexity of identifying N-ary relations. |
Controllable Paraphrase Generation with a Syntactic Exemplar (P19-1)
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| Challenge: | Prior work on controllable text generation assumes that the generated attribute can take on a finite set of values known a priori. |
| Approach: | They propose a task where the syntax of a generated sentence is controlled rather by a sentential exemplar. |
| Outcome: | The proposed model achieves improvements over baselines and learns to capture desirable characteristics. |