Felix Hill


2019

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Higher-order Comparisons of Sentence Encoder Representations
Mostafa Abdou | Artur Kulmizev | Felix Hill | Daniel M. Low | Anders Søgaard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has several advantages over existing approaches to interpretation of language encoders based on probing or diagnostic classification: namely, it does not require large training samples, is not prone to overfitting, and it enables a more transparent comparison between the representational geometries of different models and modalities. We demonstrate the utility of RSA by establishing a previously unknown correspondence between widely-employed pretrained language encoders and human processing difficulty via eye-tracking data, showcasing its potential in the interpretability toolbox for neural models.

2018

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Proceedings of the Third Workshop on Representation Learning for NLP
Isabelle Augenstein | Kris Cao | He He | Felix Hill | Spandana Gella | Jamie Kiros | Hongyuan Mei | Dipendra Misra
Proceedings of the Third Workshop on Representation Learning for NLP

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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang | Amanpreet Singh | Julian Michael | Felix Hill | Omer Levy | Samuel Bowman
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Human ability to understand language is general, flexible, and robust. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.

2017

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HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Ivan Vulić | Daniela Gerz | Douwe Kiela | Felix Hill | Anna Korhonen
Computational Linguistics, Volume 43, Issue 4 - December 2017

We introduce HyperLex—a data set and evaluation resource that quantifies the extent of the semantic category membership, that is, type-of relation, also known as hyponymy–hypernymy or lexical entailment (LE) relation between 2,616 concept pairs. Cognitive psychology research has established that typicality and category/class membership are computed in human semantic memory as a gradual rather than binary relation. Nevertheless, most NLP research and existing large-scale inventories of concept category membership (WordNet, DBPedia, etc.) treat category membership and LE as binary. To address this, we asked hundreds of native English speakers to indicate typicality and strength of category membership between a diverse range of concept pairs on a crowdsourcing platform. Our results confirm that category membership and LE are indeed more gradual than binary. We then compare these human judgments with the predictions of automatic systems, which reveals a huge gap between human performance and state-of-the-art LE, distributional and representation learning models, and substantial differences between the models themselves. We discuss a pathway for improving semantic models to overcome this discrepancy, and indicate future application areas for improved graded LE systems.

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Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
Samuel Bowman | Yoav Goldberg | Felix Hill | Angeliki Lazaridou | Omer Levy | Roi Reichart | Anders Søgaard
Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP

2016

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Learning Distributed Representations of Sentences from Unlabelled Data
Felix Hill | Kyunghyun Cho | Anna Korhonen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Learning to Understand Phrases by Embedding the Dictionary
Felix Hill | Kyunghyun Cho | Anna Korhonen | Yoshua Bengio
Transactions of the Association for Computational Linguistics, Volume 4

Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: reverse dictionaries that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.

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SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Daniela Gerz | Ivan Vulić | Felix Hill | Roi Reichart | Anna Korhonen
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Specializing Word Embeddings for Similarity or Relatedness
Douwe Kiela | Felix Hill | Stephen Clark
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation
Felix Hill | Roi Reichart | Anna Korhonen
Computational Linguistics, Volume 41, Issue 4 - December 2015

2014

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Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can’t See What I Mean
Felix Hill | Anna Korhonen
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Multi-Modal Models for Concrete and Abstract Concept Meaning
Felix Hill | Roi Reichart | Anna Korhonen
Transactions of the Association for Computational Linguistics, Volume 2

Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition. Most perceptual input to such models corresponds to concrete noun concepts and the superiority of the multi-modal approach has only been established when evaluating on such concepts. We therefore investigate which concepts can be effectively learned by multi-modal models. We show that concreteness determines both which linguistic features are most informative and the impact of perceptual input in such models. We then introduce ridge regression as a means of propagating perceptual information from concrete nouns to more abstract concepts that is more robust than previous approaches. Finally, we present weighted gram matrix combination, a means of combining representations from distinct modalities that outperforms alternatives when both modalities are sufficiently rich.

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Concreteness and Subjectivity as Dimensions of Lexical Meaning
Felix Hill | Anna Korhonen
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Improving Multi-Modal Representations Using Image Dispersion: Why Less is Sometimes More
Douwe Kiela | Felix Hill | Anna Korhonen | Stephen Clark
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Concreteness and Corpora: A Theoretical and Practical Study
Felix Hill | Douwe Kiela | Anna Korhonen
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

2012

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Beauty Before Age? Applying Subjectivity to Automatic English Adjective Ordering
Felix Hill
Proceedings of the NAACL HLT 2012 Student Research Workshop