Matthew Lamm


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Retrieval-guided Counterfactual Generation for QA
Bhargavi Paranjape | Matthew Lamm | Ian Tenney
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Deep NLP models have been shown to be brittle to input perturbations. Recent work has shown that data augmentation using counterfactuals — i.e. minimally perturbed inputs — can help ameliorate this weakness. We focus on the task of creating counterfactuals for question answering, which presents unique challenges related to world knowledge, semantic diversity, and answerability. To address these challenges, we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual evaluation and training data with minimal human supervision. Using an open-domain QA framework and question generation model trained on original task data, we create counterfactuals that are fluent, semantically diverse, and automatically labeled. Data augmentation with RGF counterfactuals improves performance on out-of-domain and challenging evaluation sets over and above existing methods, in both the reading comprehension and open-domain QA settings. Moreover, we find that RGF data leads to significant improvements in a model’s robustness to local perturbations.


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Decontextualization: Making Sentences Stand-Alone
Eunsol Choi | Jennimaria Palomaki | Matthew Lamm | Tom Kwiatkowski | Dipanjan Das | Michael Collins
Transactions of the Association for Computational Linguistics, Volume 9

Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.

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QED: A Framework and Dataset for Explanations in Question Answering
Matthew Lamm | Jennimaria Palomaki | Chris Alberti | Daniel Andor | Eunsol Choi | Livio Baldini Soares | Michael Collins
Transactions of the Association for Computational Linguistics, Volume 9

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks—post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

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Ellipsis Resolution as Question Answering: An Evaluation
Rahul Aralikatte | Matthew Lamm | Daniel Hardt | Anders Søgaard
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Most, if not all forms of ellipsis (e.g., so does Mary) are similar to reading comprehension questions (what does Mary do), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse. Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA). We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F1) and Verb Phrase Ellipsis (from 72.89 to 78.66 F1).


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Compositional Generalization in Image Captioning
Mitja Nikolaus | Mostafa Abdou | Matthew Lamm | Rahul Aralikatte | Desmond Elliott
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image–sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.


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Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts
Matthew Lamm | Arun Chaganty | Christopher D. Manning | Dan Jurafsky | Percy Liang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

To understand a sentence like “whereas only 10% of White Americans live at or below the poverty line, 28% of African Americans do” it is important not only to identify individual facts, e.g., poverty rates of distinct demographic groups, but also the higher-order relations between them, e.g., the disparity between them. In this paper, we propose the task of Textual Analogy Parsing (TAP) to model this higher-order meaning. Given a sentence such as the one above, TAP outputs a frame-style meaning representation which explicitly specifies what is shared (e.g., poverty rates) and what is compared (e.g., White Americans vs. African Americans, 10% vs. 28%) between its component facts. Such a meaning representation can enable new applications that rely on discourse understanding such as automated chart generation from quantitative text. We present a new dataset for TAP, baselines, and a model that successfully uses an ILP to enforce the structural constraints of the problem.


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Gapping Constructions in Universal Dependencies v2
Sebastian Schuster | Matthew Lamm | Christopher D. Manning
Proceedings of the NoDaLiDa 2017 Workshop on Universal Dependencies (UDW 2017)

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The Pragmatics of Indirect Commands in Collaborative Discourse
Matthew Lamm | Mihail Eric
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers