Papers by Priyanka Sen
Semantic Parsing of Disfluent Speech (2021.eacl-main)
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| Challenge: | Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text . |
| Approach: | They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance . |
| Outcome: | The proposed parser outperforms the state-of-the-art parsers on the ATIS dataset in terms of performance and accuracy. |
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs (2021.emnlp-main)
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| Challenge: | End-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) are effective, but training a weakly supervised dataset is difficult. |
| Approach: | They extend the boundaries of E2E learning for KGQA to include the training of an ER component. |
| Outcome: | The proposed model is fully differentiable thanks to a recent method for building differentiably KGs. |
What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
| Outcome: | The proposed models outperform human baselines on the widely-used SQuAD 1.1 and SQu AD 2.0 datasets. |
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection (2021.emnlp-main)
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| Challenge: | Existing models that handle single-entity questions have focused on relation following . introducing intersection improves performance on multiple-entities questions by over 14% . |
| Approach: | They propose a model that explicitly handles multiple-entity questions by implementing an intersection operation. |
| Outcome: | The proposed model improves on multiple-entity questions by over 14% on two datasets . it also improves performance on questions with multiple entities by 19% . |
Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering (2022.coling-1)
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| Challenge: | Existing question answering models can achieve high performance on simple questions that require a single fact lookup. |
| Approach: | They introduce a multilingual question-answering dataset called Mintaka . it includes 8 types of complex questions, including superlative, intersection, and multi-hop questions . they run baselines over Mintak, which achieves 38% hits@1 in English . |
| Outcome: | The proposed model achieves 38% hits@1 in English and 31% hits@1, multilingually. |