Aparajita Haldar


2019

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Characterizing the Impact of Geometric Properties of Word Embeddings on Task Performance
Brendan Whitaker | Denis Newman-Griffis | Aparajita Haldar | Hakan Ferhatosmanoglu | Eric Fosler-Lussier
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP

Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors. However, geometric properties of the continuous feature space contribute directly to the use of embedding features in downstream models, and are largely unexplored. We consider four properties of word embedding geometry, namely: position relative to the origin, distribution of features in the vector space, global pairwise distances, and local pairwise distances. We define a sequence of transformations to generate new embeddings that expose subsets of these properties to downstream models and evaluate change in task performance to understand the contribution of each property to NLP models. We transform publicly available pretrained embeddings from three popular toolkits (word2vec, GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model linguistic information in the vector space, and extrinsic tasks, which use vectors as input to machine learning models. We find that intrinsic evaluations are highly sensitive to absolute position, while extrinsic tasks rely primarily on local similarity. Our findings suggest that future embedding models and post-processing techniques should focus primarily on similarity to nearby points in vector space.

2018

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Hypothesis Only Baselines in Natural Language Inference
Adam Poliak | Jason Naradowsky | Aparajita Haldar | Rachel Rudinger | Benjamin Van Durme
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.

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Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak | Aparajita Haldar | Rachel Rudinger | J. Edward Hu | Ellie Pavlick | Aaron Steven White | Benjamin Van Durme
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

We present a large scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation encoded by a neural network captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. Our collection of diverse datasets is available at http://www.decomp.net/, and will grow over time as additional resources are recast and added from novel sources.