519 citations · 4.1k across the 233 of their papers we have counts for
35 papers · 1 filter
Towards a General-Purpose Linguistic Annotation Backend
Graham Neubig, Patrick Littell, Chian-Yu Chen +4
Language documentation is inherently a time-intensive process; transcription, glossing, and corpus management consume a significant portion of documentary linguists' work. Advances…
Learning to Generate Corrective Patches using Neural Machine Translation
Hideaki Hata, Emad Shihab, Graham Neubig
Bug fixing is generally a manually-intensive task. However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in diffe…
Zero-shot Neural Transfer for Cross-lingual Entity Linking
Shruti Rijhwani, Jiateng Xie, Graham Neubig +1
Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While p…
Multi-Source Neural Machine Translation with Data Augmentation
Yuta Nishimura, Katsuhito Sudoh, Graham Neubig +1
Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains…
Learning to Describe Phrases with Local and Global Contexts
Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga +4
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entit…
Optimizing Segmentation Granularity for Neural Machine Translation
Elizabeth Salesky, Andrew Runge, Alex Coda +2
In neural machine translation (NMT), it is has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair…
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Pengcheng Yin, Graham Neubig
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system bas…
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis +2
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient informat…
Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
Devendra Singh Sachan, Graham Neubig
In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even…
Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Jiateng Xie, Zhilin Yang, Graham Neubig +2
For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be…
MTNT: A Testbed for Machine Translation of Noisy Text
Paul Michel, Graham Neubig
Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating nois…
Contextual Encoding for Translation Quality Estimation
Junjie Hu, Wei-Cheng Chang, Yuexin Wu +1
The task of word-level quality estimation (QE) consists of taking a source sentence and machine-generated translation, and predicting which words in the output are correct and whic…
Retrieval-Based Neural Code Generation
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru +3
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to gene…
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Aditi Chaudhary, Chunting Zhou, Lori Levin +3
Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches f…
A Tree-based Decoder for Neural Machine Translation
Xinyi Wang, Hieu Pham, Pengcheng Yin +1
Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes…
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models h…
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Xinyi Wang, Hieu Pham, Zihang Dai +1
In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with des…
Contextual Parameter Generation for Universal Neural Machine Translation
Emmanouil Antonios Platanios, Mrinmaya Sachan, Graham Neubig +1
We propose a simple modification to existing neural machine translation (NMT) models that enables using a single universal model to translate between multiple languages while allow…
Rapid Adaptation of Neural Machine Translation to New Languages
Graham Neubig, Junjie Hu
This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods bas…
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Pengcheng Yin, Chunting Zhou, Junxian He +1
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utter…
Findings of the Second Workshop on Neural Machine Translation and Generation
Alexandra Birch, Andrew Finch, Minh-Thang Luong +2
This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computat…
Stress Test Evaluation for Natural Language Inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh +2
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner. NLI was proposed as a ben…
Multi-Source Neural Machine Translation with Missing Data
Yuta Nishimura, Katsuhito Sudoh, Graham Neubig +1
Multi-source translation is an approach to exploit multiple inputs (e.g. in two different languages) to increase translation accuracy. In this paper, we examine approaches for mult…
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
Pengcheng Yin, Bowen Deng, Edgar Chen +2
For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require par…