Search Results for author: David Reitter

Found 23 papers, 2 papers with code

Are BERTs Sensitive to Native Interference in L2 Production?

no code implementations EMNLP (insights) 2021 Zixin Tang, Prasenjit Mitra, David Reitter

With the essays part from The International Corpus Network of Asian Learners of English (ICNALE) and the TOEFL11 corpus, we fine-tuned neural language models based on BERT to predict English learners’ native languages.

Surprisal Predicts Code-Switching in Chinese-English Bilingual Text

no code implementations EMNLP 2020 Jes{\'u}s Calvillo, Le Fang, Jeremy Cole, David Reitter

We also found sentence length to be a significant predictor, which has been related to sentence complexity.

Measuring Attribution in Natural Language Generation Models

no code implementations23 Dec 2021 Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, Michael Collins, Dipanjan Das, Slav Petrov, Gaurav Singh Tomar, Iulia Turc, David Reitter

With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world.

Text Generation

CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning

no code implementations16 Dec 2021 Zeqiu Wu, Yi Luan, Hannah Rashkin, David Reitter, Hannaneh Hajishirzi, Mari Ostendorf, Gaurav Singh Tomar

Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.

Conversational Question Answering Passage Retrieval +1

Evaluating Groundedness in Dialogue Systems: The BEGIN Benchmark

no code implementations30 Apr 2021 Nouha Dziri, Hannah Rashkin, Tal Linzen, David Reitter

To facilitate evaluation of such metrics, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN).

Language Modelling Natural Language Inference

Do We Need Neural Models to Explain Human Judgments of Acceptability?

no code implementations18 Sep 2019 Wang Jing, M. A. Kelly, David Reitter

We test the ability of computational language models, simple language features, and word embeddings to predict native English speakers judgments of acceptability on English-language essays written by non-native speakers.

Word Embeddings Word Similarity

Fusion of Detected Objects in Text for Visual Question Answering

1 code implementation IJCNLP 2019 Chris Alberti, Jeffrey Ling, Michael Collins, David Reitter

To advance models of multimodal context, we introduce a simple yet powerful neural architecture for data that combines vision and natural language.

Question Answering Visual Commonsense Reasoning +1

Treat the Word As a Whole or Look Inside? Subword Embeddings Model Language Change and Typology

1 code implementation WS 2019 Yang Xu, Jiasheng Zhang, David Reitter

We use a variant of word embedding model that incorporates subword information to characterize the degree of compositionality in lexical semantics.

The Timing of Lexical Memory Retrievals in Language Production

no code implementations NAACL 2018 Jeremy Cole, David Reitter

This paper explores the time course of lexical memory retrieval by modeling fluent language production.

Learning to Adapt by Minimizing Discrepancy

no code implementations30 Nov 2017 Alexander G. Ororbia II, Patrick Haffner, David Reitter, C. Lee Giles

We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences.

Event Ordering with a Generalized Model for Sieve Prediction Ranking

no code implementations IJCNLP 2017 Bill McDowell, Nathanael Chambers, Alex Ororbia II, er, David Reitter

Within this prediction reranking framework, we propose an alternative scoring function, showing an 8. 8{\%} relative gain over the original CAEVO.

Word Embeddings

Spectral Analysis of Information Density in Dialogue Predicts Collaborative Task Performance

no code implementations ACL 2017 Yang Xu, David Reitter

We propose a perspective on dialogue that focuses on relative information contributions of conversation partners as a key to successful communication.

Learning Simpler Language Models with the Differential State Framework

no code implementations26 Mar 2017 Alexander G. Ororbia II, Tomas Mikolov, David Reitter

The Differential State Framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models.

Language Modelling

Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders

no code implementations22 Nov 2015 Alexander G. Ororbia II, C. Lee Giles, David Reitter

Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems.

Denoising

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