James Glass

Recurrent Neural Network Encoder with Attention for Community Question Answering

2016 · arXiv (Cornell University)

We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further …

Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign

2018 · Työväentutkimus Vuosikirja

We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of …

Domain Attentive Fusion for End-to-end Dialect Identification with\n Unknown Target Domain

2018 · arXiv (Cornell University)

End-to-end deep learning language or dialect identification systems operate\non the spectrogram or other acoustic feature and directly generate\nidentification scores for each class. An important issue for end-to-end systems\nis to have some knowledge of the application …

Logic Against Bias: Textual Entailment Mitigates Stereotypical Sentence Reasoning

2023 · arXiv (Cornell University)

Due to their similarity-based learning objectives, pretrained sentence encoders often internalize stereotypical assumptions that reflect the social biases that exist within their training corpora. In this paper, we describe several kinds of stereotypes concerning different …

Detecting Dementia from Long Neuropsychological Interviews

2022

Neuropsychological exams are commonly used to diagnose various kinds of cognitive impairment. They typically involve a trained examiner who conducts a series of cognitive tests with a subject. In recent years, there has been growing …

DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

2023 · arXiv (Cornell University)

Despite their impressive capabilities, large language models (LLMs) are prone to hallucinations, i.e., generating content that deviates from facts seen during pretraining. We propose a simple decoding strategy for reducing hallucinations with pretrained LLMs that …

R-Spin: Efficient Speaker and Noise-invariant Representation Learning with Acoustic Pieces

2023 · arXiv (Cornell University)

This paper introduces Robust Spin (R-Spin), a data-efficient domain-specific self-supervision method for speaker and noise-invariant speech representations by learning discrete acoustic units with speaker-invariant clustering (Spin). R-Spin resolves Spin's issues and enhances content representations by …

Unsupervised Lexicon Discovery from Acoustic Input

2015 · Transactions of the Association for Computational Linguistics

We present a model of unsupervised phonological lexicon discovery—the problem of simultaneously learning phoneme-like and word-like units from acoustic input. Our model builds on earlier models of unsupervised phone-like unit discovery from acoustic data (Lee …

What do Neural Machine Translation Models Learn about Morphology?

2017

Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process.

Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks

2017 · International Joint Conference on Natural Language Processing

While neural machine translation (NMT) models provide improved translation quality in an elegant framework, it is less clear what they learn about language. Recent work has started evaluating the quality of vector representations learned by …

Convolutional Neural Network and Language Embeddings for End-to-End Dialect Recognition

2018

Dialect identification (DID) is a special case of general language identification (LID), but a more challenging problem due to the linguistic similarity between dialects. In this paper, we propose an end-to-end DID system and a …

Supervised and Unsupervised Transfer Learning for Question Answering

2018

Yu-An Chung, Hung-Yi Lee, James Glass. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

2019

Despite the remarkable evolution of deep neural networks in natural language processing (NLP), their interpretability remains a challenge. Previous work largely focused on what these models learn at the representation level. We break this analysis …