Felix Hill

Analysing Mathematical Reasoning Abilities of Neural Models

2019 · arXiv (Cornell University)

Mathematical reasoning---a core ability within human intelligence---presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis …

Learning to Understand Phrases by Embedding the Dictionary

2015 · arXiv (Cornell University)

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 …

Learning to Understand Phrases by Embedding the Dictionary

2016 · Transactions of the Association for Computational Linguistics

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 …

SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation

2015 · Computational Linguistics

We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways. First, in contrast to gold standards such as WordSim-353 and MEN, it explicitly quantifies …

Specializing Word Embeddings for Similarity or Relatedness

2015

We demonstrate the advantage of specializing semantic word embeddings for either similarity or relatedness. We compare two variants of retrofitting and a joint-learning approach, and find that all three yield specialized semantic spaces that capture …

Learning Distributed Representations of Sentences from Unlabelled Data

2016

Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper …

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

2018 · arXiv (Cornell University)

For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is …

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

2019 · arXiv (Cornell University)

In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, …

The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations

2016 · arXiv (Cornell University)

Abstract: We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lower-frequency …