Search Results for author: John Wieting

Found 28 papers, 15 papers with code

RankGen: Improving Text Generation with Large Ranking Models

1 code implementation19 May 2022 Kalpesh Krishna, Yapei Chang, John Wieting, Mohit Iyyer

Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts.

Contrastive Learning Language Modelling +1

Faithful to the Document or to the World? Mitigating Hallucinations via Entity-linked Knowledge in Abstractive Summarization

no code implementations28 Apr 2022 Yue Dong, John Wieting, Pat Verga

In this work, we show that these entities are not aberrations, but they instead require utilizing external world knowledge to infer reasoning paths from entities in the source.

Abstractive Text Summarization

Paraphrastic Representations at Scale

1 code implementation30 Apr 2021 John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick

We train these models on large amounts of data, achieving significantly improved performance from the original papers proposing the methods on a suite of monolingual semantic similarity, cross-lingual semantic similarity, and bitext mining tasks.

Semantic Similarity Semantic Textual Similarity

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

3 code implementations11 Mar 2021 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.

On Learning Text Style Transfer with Direct Rewards

1 code implementation NAACL 2021 Yixin Liu, Graham Neubig, John Wieting

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.

Machine Translation Semantic Similarity +4

Reformulating Unsupervised Style Transfer as Paraphrase Generation

1 code implementation EMNLP 2020 Kalpesh Krishna, John Wieting, Mohit Iyyer

Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs.

Paraphrase Generation Pretrained Language Models +1

Improving Candidate Generation for Low-resource Cross-lingual Entity Linking

1 code implementation TACL 2020 Shuyan Zhou, Shruti Rijhawani, John Wieting, Jaime Carbonell, Graham Neubig

Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts.

Cross-Lingual Entity Linking Entity Linking +1

A Bilingual Generative Transformer for Semantic Sentence Embedding

2 code implementations EMNLP 2020 John Wieting, Graham Neubig, Taylor Berg-Kirkpatrick

Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embedding space indicates closeness in the semantics between the sentences.

Semantic Similarity Semantic Textual Similarity +2

Simple and Effective Paraphrastic Similarity from Parallel Translations

4 code implementations ACL 2019 John Wieting, Kevin Gimpel, Graham Neubig, Taylor Berg-Kirkpatrick

We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora.

Sentence Embeddings

Beyond BLEU: Training Neural Machine Translation with Semantic Similarity

1 code implementation14 Sep 2019 John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, Graham Neubig

While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy.

Machine Translation Semantic Similarity +2

Beyond BLEU:Training Neural Machine Translation with Semantic Similarity

no code implementations ACL 2019 John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, Graham Neubig

While most neural machine translation (NMT)systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can significantly improve final translation accuracy.

Machine Translation Semantic Similarity +2

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

2 code implementations NAACL 2019 Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, Xinyi Wang, John Wieting

In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation.

Machine Translation Text Generation +1

No Training Required: Exploring Random Encoders for Sentence Classification

1 code implementation ICLR 2019 John Wieting, Douwe Kiela

We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i. e., using nothing but random parameterizations.

Classification General Classification +3

Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

2 code implementations NAACL 2018 Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer

We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples.

Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext

no code implementations EMNLP 2017 John Wieting, Jonathan Mallinson, Kevin Gimpel

We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of Wieting et al. (2016b).

Machine Translation Sentence Embeddings +1

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

no code implementations ACL 2017 John Wieting, Kevin Gimpel

We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b).

Sentence Embeddings Transfer Learning

Charagram: Embedding Words and Sentences via Character n-grams

no code implementations EMNLP 2016 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu

We present Charagram embeddings, a simple approach for learning character-based compositional models to embed textual sequences.

Part-Of-Speech Tagging Sentence Similarity +1

Towards Universal Paraphrastic Sentence Embeddings

no code implementations25 Nov 2015 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu

We again find that the word averaging models perform well for sentence similarity and entailment, outperforming LSTMs.

General Classification Sentence Embeddings +2

Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm

no code implementations25 Aug 2015 Daniel Khashabi, John Wieting, Jeffrey Yufei Liu, Feng Liang

Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values.

From Paraphrase Database to Compositional Paraphrase Model and Back

1 code implementation TACL 2015 John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, Dan Roth

The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates.

Word Embeddings

Tiered Clustering to Improve Lexical Entailment

no code implementations2 Dec 2014 John Wieting

The second is a supervised approach where a classifier is learned to predict entailment given a concatenated latent vector representation of the word.

Lexical Entailment

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