105 citations · 349 across the 67 of their papers we have counts for
10 papers · 1 filter
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences
Eleftheria Briakou, Navita Goyal, Marine Carpuat
Explainable NLP techniques primarily explain by answering "Which tokens in the input are responsible for this prediction?''. We argue that for NLP models that make predictions by c…
Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
HyoJung Han, Jordan Lee Boyd-Graber, Marine Carpuat
Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary backgrou…
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
Sweta Agrawal, Marine Carpuat
Automatic text simplification (TS) aims to automate the process of rewriting text to make it easier for people to read. A pre-requisite for TS to be useful is that it should convey…
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Elijah Rippeth, Marine Carpuat, Kevin Duh +1
Lexical ambiguity is a challenging and pervasive problem in machine translation (\mt). We introduce a simple and scalable approach to resolve translation ambiguity by incorporating…
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang, David Ifeoluwa Adelani, Sweta Agrawal +55
Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, s…
Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors
Nikita Mehandru, Sweta Agrawal, Yimin Xiao +4
A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs. Progress in quality estimat…
Towards Conceptualization of "Fair Explanation": Disparate Impacts of anti-Asian Hate Speech Explanations on Content Moderators
Tin Nguyen, Jiannan Xu, Aayushi Roy +2
Recent research at the intersection of AI explainability and fairness has focused on how explanations can improve human-plus-AI task performance as assessed by fairness measures. W…
Controlling Pre-trained Language Models for Grade-Specific Text Simplification
Sweta Agrawal, Marine Carpuat
Text simplification (TS) systems rewrite text to make it more readable while preserving its content. However, what makes a text easy to read depends on the intended readers. Recent…
What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems
Navita Goyal, Eleftheria Briakou, Amanda Liu +6
NLP systems have shown impressive performance at answering questions by retrieving relevant context. However, with the increasingly large models, it is impossible and often undesir…
Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
Weijia Xu, Sweta Agrawal, Eleftheria Briakou +2
Neural sequence generation models are known to "hallucinate", by producing outputs that are unrelated to the source text. These hallucinations are potentially harmful, yet it remai…