Papers by Tania Bedrax-Weiss
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary (2021.tacl-1)
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| Challenge: | Existing text overlap based evaluation metrics are limited to matching tokens, either lexically or via embeddings. |
| Approach: | They propose a metric to evaluate the content quality of a summary using question-answering (QA) QA-based methods directly measure a summary’s information overlap with a reference, making them fundamentally different from text overlap metrics. |
| Outcome: | The proposed metric outperforms current state-of-the-art metrics on most evaluations using benchmark datasets while being competitive on others due to limitations of state- of-the art models. |
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)
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| Challenge: | Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems. |
| Approach: | They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer. |
| Outcome: | The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths . |
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2023.acl-long)
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Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
| Challenge: | Existing DSI approaches infer latent dialog structure without access to domain knowledge. |
| Approach: | They propose a neural-symbolic approach that injects symbolic knowledge into latent space of a generative neural model. |
| Outcome: | The proposed approach boosts performance over the canonical baselines over three dialog structure induction datasets. |
How Large Are Lions? Inducing Distributions over Quantitative Attributes (P19-1)
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| Challenge: | Current NLP systems have little knowledge about quantitative attributes of objects and events. |
| Approach: | They propose to use web data to create a resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs. |
| Outcome: | The proposed method compares favorably with state-of-the-art results on existing datasets for relative comparisons of nouns and adjectives and on a new dataset. |