Matthew Lamm


2019

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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.

2018

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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.

2017

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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