519 citations · 4.1k across the 233 of their papers we have counts for
45 papers · 1 filter
Merging Weak and Active Supervision for Semantic Parsing
Ansong Ni, Pengcheng Yin, Graham Neubig
A semantic parser maps natural language commands (NLs) from the users to executable meaning representations (MRs), which are later executed in certain environment to obtain user-de…
How Can We Know What Language Models Know?
Zhengbao Jiang, Frank F. Xu, Jun Araki +1
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profe…
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang, Hieu Pham, Paul Michel +3
To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content…
A Bilingual Generative Transformer for Semantic Sentence Embedding
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…
Generalizing Natural Language Analysis through Span-relation Representations
Zhengbao Jiang, Wei Xu, Jun Araki +1
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially design…
Should All Cross-Lingual Embeddings Speak English?
Antonios Anastasopoulos, Graham Neubig
Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another langua…
Understanding Knowledge Distillation in Non-autoregressive Machine Translation
Chunting Zhou, Graham Neubig, Jiatao Gu
Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregress…
Findings of the Third Workshop on Neural Generation and Translation
Hiroaki Hayashi, Yusuke Oda, Alexandra Birch +5
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Lang…
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Xuezhe Ma, Chunting Zhou, Xian Li +2
Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, non-autoregressive seq2seq mode…
Domain Differential Adaptation for Neural Machine Translation
Zi-Yi Dou, Xinyi Wang, Junjie Hu +1
Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This…
DIRE: A Neural Approach to Decompiled Identifier Naming
Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz +4
The decompiler is one of the most common tools for examining binaries without corresponding source code. It transforms binaries into high-level code, reversing the compilation proc…
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
Zirui Wang, Jiateng Xie, Ruochen Xu +3
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks. There are two main paradigms for learning such represen…
Towards Zero-resource Cross-lingual Entity Linking
Shuyan Zhou, Shruti Rijhwani, Graham Neubig
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because o…
Simple and Effective Paraphrastic Similarity from Parallel Translations
John Wieting, Kevin Gimpel, Graham Neubig +1
We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating paraphrase corpora.…
Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel +1
While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve…
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
Bohan Li, Junxian He, Graham Neubig +2
When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework. In practice, however, VAEs are tra…
Learning to Deceive with Attention-Based Explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra +2
Attention mechanisms are ubiquitous components in neural architectures applied to natural language processing. In addition to yielding gains in predictive accuracy, attention weigh…
What Makes A Good Story? Designing Composite Rewards for Visual Storytelling
Junjie Hu, Yu Cheng, Zhe Gan +3
Previous storytelling approaches mostly focused on optimizing traditional metrics such as BLEU, ROUGE and CIDEr. In this paper, we re-examine this problem from a different angle, b…
Handling Syntactic Divergence in Low-resource Machine Translation
Chunting Zhou, Xuezhe Ma, Junjie Hu +1
Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pair…
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
Zi-Yi Dou, Junjie Hu, Antonios Anastasopoulos +1
The recent success of neural machine translation models relies on the availability of high quality, in-domain data. Domain adaptation is required when domain-specific data is scarc…
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers
Aditi Chaudhary, Jiateng Xie, Zaid Sheikh +2
Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lower-resourced…
Latent Relation Language Models
Hiroaki Hayashi, Zecong Hu, Chenyan Xiong +1
In this paper, we propose Latent Relation Language Models (LRLMs), a class of language models that parameterizes the joint distribution over the words in a document and the entitie…
Pushing the Limits of Low-Resource Morphological Inflection
Antonios Anastasopoulos, Graham Neubig
Recent years have seen exceptional strides in the task of automatic morphological inflection generation. However, for a long tail of languages the necessary resources are hard to c…
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg +2
Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isomet…