In collaboration with Postpartum Support International (PSI), a non-prof...
Modern language models have the capacity to store and use immense amount...
We present the first unified study of the efficiency of self-attention-b...
Long-form question answering systems provide rich information by present...
Long-form question answering (LFQA) enables answering a wide range of
qu...
We propose an unsupervised speech-to-speech translation (S2ST) system th...
While large language models are able to retain vast amounts of world
kno...
We study continually improving an extractive question answering (QA) sys...
Evidence retrieval is a core part of automatic fact-checking. Prior work...
Pre-trained language models (LMs) are used for knowledge intensive tasks...
Identifying the difference between two versions of the same article is u...
We study the problem of classification with a reject option for a fixed
...
Digital platforms, including online forums and helplines, have emerged a...
Automatic speech recognition research focuses on training and evaluating...
We study politeness phenomena in nine typologically diverse languages.
P...
While the NLP community is generally aware of resource disparities among...
Recent visuolinguistic pre-trained models show promising progress on var...
Question answering models can use rich knowledge sources – up to one hun...
Developing methods to adversarially challenge NLP systems is a promising...
Exemplification is a process by which writers explain or clarify a conce...
Verifying complex political claims is a challenging task, especially whe...
Language models (LMs) are typically trained once on a large-scale corpus...
Long-form answers, consisting of multiple sentences, can provide nuanced...
We study learning from user feedback for extractive question answering b...
Answers to the same question may change depending on the extra-linguisti...
Training NLP systems typically assumes access to annotated data that has...
Most benchmark datasets targeting commonsense reasoning focus on everyda...
We study calibration in question answering, estimating whether model
cor...
Prior beliefs of readers impact the way in which they project meaning on...
To build robust question answering systems, we need the ability to verif...
We study estimating inherent human disagreement (annotation label
distri...
Models for question answering, dialogue agents, and summarization often
...
We review the EfficientQA competition from NeurIPS 2020. The competition...
Recent progress in pretrained language model "solved" many reading
compr...
Multilingual question answering tasks typically assume answers exist in ...
A question answering system that in addition to providing an answer prov...
We focus on the problem of capturing declarative knowledge in the learne...
Confidently making progress on multilingual modeling requires challengin...
We present the results of the Machine Reading for Question Answering (MR...
Understanding the dynamics of international politics is important yet
ch...
Reasoning about implied relationships (e.g. paraphrastic, common sense,
...
Conversational machine comprehension requires a deep understanding of th...
We present end-to-end neural models for detecting metaphorical word use ...
We present QuAC, a dataset for Question Answering in Context that contai...
We introduce a new entity typing task: given a sentence with an entity
m...
We show that relation extraction can be reduced to answering simple read...
We present TriviaQA, a challenging reading comprehension dataset contain...
We present a framework for question answering that can efficiently scale...