519 citations · 4.1k across the 233 of their papers we have counts for
37 papers · 1 filter
Interpretable Multi-dataset Evaluation for Named Entity Recognition
Jinlan Fu, Pengfei Liu, Graham Neubig
With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits. Simply looking at…
How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
Zhengbao Jiang, Jun Araki, Haibo Ding +1
Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to pro…
Evaluating Explanations: How much do explanations from the teacher aid students?
Danish Pruthi, Rachit Bansal, Bhuwan Dhingra +5
While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this…
Decoding and Diversity in Machine Translation
Nicholas Roberts, Davis Liang, Graham Neubig +1
Neural Machine Translation (NMT) systems are typically evaluated using automated metrics that assess the agreement between generated translations and ground truth candidates. To im…
Reducing Confusion in Active Learning for Part-Of-Speech Tagging
Aditi Chaudhary, Antonios Anastasopoulos, Zaid Sheikh +1
Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntacti…
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
Hiroaki Hayashi, Prashant Budania, Peng Wang +3
Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understa…
OCR Post Correction for Endangered Language Texts
Shruti Rijhwani, Antonios Anastasopoulos, Graham Neubig
There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats tha…
Weakly- and Semi-supervised Evidence Extraction
Danish Pruthi, Bhuwan Dhingra, Graham Neubig +1
For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, additional an…
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki +2
Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as "Punta Cana is located in _."…
NeuSpell: A Neural Spelling Correction Toolkit
Sai Muralidhar Jayanthi, Danish Pruthi, Graham Neubig
We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspelling…
Re-evaluating Evaluation in Text Summarization
Manik Bhandari, Pranav Gour, Atabak Ashfaq +2
Automated evaluation metrics as a stand-in for manual evaluation are an essential part of the development of text-generation tasks such as text summarization. However, while the fi…
Automatic Extraction of Rules Governing Morphological Agreement
Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa +4
Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. I…
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation
Luyu Gao, Xinyi Wang, Graham Neubig
To improve the performance of Neural Machine Translation~(NMT) for low-resource languages~(LRL), one effective strategy is to leverage parallel data from a related high-resource la…
On Learning Text Style Transfer with Direct Rewards
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. In this paper, we explore training algorithms…
GSum: A General Framework for Guided Neural Abstractive Summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi +2
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies a…
Explicit Alignment Objectives for Multilingual Bidirectional Encoders
Junjie Hu, Melvin Johnson, Orhan Firat +2
Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP…
The Return of Lexical Dependencies: Neural Lexicalized PCFGs
Hao Zhu, Yonatan Bisk, Graham Neubig
In this paper we demonstrate that . This contrasts to the most p…
TICO-19: the Translation Initiative for Covid-19
Antonios Anastasopoulos, Alessandro Cattelan, Zi-Yi Dou +15
The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations t…
Learning Sparse Prototypes for Text Generation
Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig
Prototype-driven text generation uses non-parametric models that first choose from a library of sentence "prototypes" and then modify the prototype to generate the output text. Whi…
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Pengcheng Yin, Graham Neubig, Wen-tau Yih +1
Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-f…
Soft Gazetteers for Low-Resource Named Entity Recognition
Shruti Rijhwani, Shuyan Zhou, Graham Neubig +1
Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-cr…
Predicting Performance for Natural Language Processing Tasks
Mengzhou Xia, Antonios Anastasopoulos, Ruochen Xu +2
Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly pr…
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Frank F. Xu, Lei Ji, Botian Shi +4
Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirec…
Politeness Transfer: A Tag and Generate Approach
Aman Madaan, Amrith Setlur, Tanmay Parekh +6
This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset o…