519 citations · 4.1k across the 233 of their papers we have counts for
46 papers · 1 filter
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Qibin Chen, Jeremy Lacomis, Edward J. Schwartz +3
Variable names are critical for conveying intended program behavior. Machine learning-based program analysis methods use variable name representations for a wide range of tasks, su…
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations
Siddhant Arora, Danish Pruthi, Norman Sadeh +3
In attempts to "explain" predictions of machine learning models, researchers have proposed hundreds of techniques for attributing predictions to features that are deemed important.…
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho +1
It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation…
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos +1
Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce…
On The Ingredients of an Effective Zero-shot Semantic Parser
Pengcheng Yin, John Wieting, Avirup Sil +1
Semantic parsers map natural language utterances into meaning representations (e.g., programs). Such models are typically bottlenecked by the paucity of training data due to the re…
Systematic Inequalities in Language Technology Performance across the World's Languages
Damián Blasi, Antonios Anastasopoulos, Graham Neubig
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and de…
Breaking Down Multilingual Machine Translation
Ting-Rui Chiang, Yi-Pei Chen, Yi-Ting Yeh +1
While multilingual training is now an essential ingredient in machine translation (MT) systems, recent work has demonstrated that it has different effects in different multilingual…
Towards a Unified View of Parameter-Efficient Transfer Learning
Junxian He, Chunting Zhou, Xuezhe Ma +2
Fine-tuning large pre-trained language models on downstream tasks has become the de-facto learning paradigm in NLP. However, conventional approaches fine-tune all the parameters of…
Capturing Structural Locality in Non-parametric Language Models
Frank F. Xu, Junxian He, Graham Neubig +1
Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or proj…
Dependency Induction Through the Lens of Visual Perception
Ruisi Su, Shruti Rijhwani, Hao Zhu +4
Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recentl…
When is Wall a Pared and when a Muro? -- Extracting Rules Governing Lexical Selection
Aditi Chaudhary, Kayo Yin, Antonios Anastasopoulos +1
Learning fine-grained distinctions between vocabulary items is a key challenge in learning a new language. For example, the noun "wall" has different lexical manifestations in Span…
Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
Xinyi Wang, Yulia Tsvetkov, Sebastian Ruder +1
Adapters are light-weight modules that allow parameter-efficient fine-tuning of pretrained models. Specialized language and task adapters have recently been proposed to facilitate…
AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages
Machel Reid, Junjie Hu, Graham Neubig +1
Reproducible benchmarks are crucial in driving progress of machine translation research. However, existing machine translation benchmarks have been mostly limited to high-resource…
Distributionally Robust Multilingual Machine Translation
Chunting Zhou, Daniel Levy, Xian Li +2
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of…
Learning to Superoptimize Real-world Programs
Alex Shypula, Pengcheng Yin, Jeremy Lacomis +3
Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive…
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language
Shuyan Zhou, Pengcheng Yin, Graham Neubig
When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks. However, in the literature on natural languag…
When Does Translation Require Context? A Data-driven, Multilingual Exploration
Patrick Fernandes, Kayo Yin, Emmy Liu +2
Although proper handling of discourse significantly contributes to the quality of machine translation (MT), these improvements are not adequately measured in common translation qua…
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Lucio M. Dery, Paul Michel, Ameet Talwalkar +1
In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks. However, widely used methods for levera…
Efficient Nearest Neighbor Language Models
Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore, which allows them to learn through explicitly memorizing the tr…
Augmenting Decompiler Output with Learned Variable Names and Types
Qibin Chen, Jeremy Lacomis, Edward J. Schwartz +3
A common tool used by security professionals for reverse-engineering binaries found in the wild is the decompiler. A decompiler attempts to reverse compilation, transforming a bina…
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu +3
This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning". Unlike traditional supervised learning, whic…
Few-shot Language Coordination by Modeling Theory of Mind
Hao Zhu, Graham Neubig, Yonatan Bisk
Humans communicate with a large community by coordinating with different interlocutors within short conversations. This ability has been understudie…
Phrase-level Active Learning for Neural Machine Translation
Junjie Hu, Graham Neubig
Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating…
Examining and Combating Spurious Features under Distribution Shift
Chunting Zhou, Xuezhe Ma, Paul Michel +1
A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical…