Papers by Matthew Lamm

5 papers
Decontextualization: Making Sentences Stand-Alone (2021.tacl-1)

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Challenge: Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window.
Approach: They define a problem of sentence decontextualization by rewriting a sentence to be interpretable out of context while preserving its meaning.
Outcome: The proposed method can be used in question answering and document understanding tasks.
QED: A Framework and Dataset for Explanations in Question Answering (2021.tacl-1)

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Challenge: Existing question answering systems provide no explanation of reasoning that leads to answer . linguistically informed, extensible framework provides explanations in question answering .
Approach: They propose a linguistically informed, extensible framework for explanations in question answering . they propose an expert-annotated dataset of QED explanations built upon a subset of the Natural Questions dataset .
Outcome: The proposed framework improves the ability of untrained raters to spot errors in QA datasets.
Retrieval-guided Counterfactual Generation for QA (2022.acl-long)

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Challenge: Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness.
Approach: They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data.
Outcome: The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods.
Ellipsis Resolution as Question Answering: An Evaluation (2021.eacl-main)

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Challenge: Existing models for ellipsis resolution in English are expensive and cumbersome . ellipas are hard, open problems in NLP, and can cause errors in translation, question answering, and dialogue understanding.
Approach: They propose an alternative approach to ellipsis resolution based on question answering architectures.
Outcome: The proposed model outperforms the current state of the art for ellipsis resolution in English . it shows that annotations can be useful for a subset of the known ellipas .
Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (D18-1)

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Challenge: Existing methods to extract information from text do not capture disparity between demographic groups.
Approach: They propose a task of Textual Analogy Parsing to model higher-order meanings by comparing poverty rates between different demographic groups.
Outcome: The proposed model can be used to generate graphs from quantitative text.

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