Search Results for author:
Found 42 papers, 22 papers with code
Retrieval Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
CapWAP: Image Captioning with a Purpose
In this task, we use question-answer (QA) pairs{---}a natural expression of information need{---}from users, instead of reference captions, for both training and post-inference evaluation.
Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong generalization on various visual domains and tasks.
DePlot: One-shot visual language reasoning by plot-to-table translation
Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Ranked #1 on
Chart Question Answering
on ChartQA
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
Visual language data such as plots, charts, and infographics are ubiquitous in the human world.
Ranked #1 on
Visual Question Answering (VQA)
on PlotQA-D1
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Visually-situated language is ubiquitous -- sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and forms.
Ranked #3 on
Chart Question Answering
on ChartQA
Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer
Zero-shot cross-lingual transfer is emerging as a practical solution: pre-trained models later fine-tuned on one transfer language exhibit surprising performance when tested on many target languages.
Joint Passage Ranking for Diverse Multi-Answer Retrieval
We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question.
Neural Data Augmentation via Example Extrapolation
In many applications of machine learning, certain categories of examples may be underrepresented in the training data, causing systems to underperform on such "few-shot" cases at test time.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
CapWAP: Captioning with a Purpose
In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation.
High Performance Natural Language Processing
Scale has played a central role in the rapid progress natural language processing has enjoyed in recent years.
XOR QA: Cross-lingual Open-Retrieval Question Answering
Multilingual question answering tasks typically assume answers exist in the same language as the question.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training.
Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering
We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings.
Contextualized Representations Using Textual Encyclopedic Knowledge
We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents.
REALM: Retrieval-Augmented Language Model Pre-Training
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering.
Ranked #8 on
Question Answering
on WebQuestions
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers.
Ranked #3 on
Question Answering
on DROP Test
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.
Zero-Shot Entity Linking by Reading Entity Descriptions
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
Latent Retrieval for Weakly Supervised Open Domain Question Answering
We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system.
Ranked #9 on
Question Answering
on WebQuestions
Natural Questions: a Benchmark for Question Answering Research
The public release consists of 307, 373 training examples with single annotations, 7, 830 examples with 5-way annotations for development data, and a further 7, 842 examples 5-way annotated sequestered as test data.
Ranked #7 on
Question Answering
on Natural Questions (long)
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings.
Language Model Pre-training for Hierarchical Document Representations
Hierarchical neural architectures are often used to capture long-distance dependencies and have been applied to many document-level tasks such as summarization, document segmentation, and sentiment analysis.
A BERT Baseline for the Natural Questions
This technical note describes a new baseline for the Natural Questions.
Ranked #6 on
Question Answering
on Natural Questions (long)
Improving Span-based Question Answering Systems with Coarsely Labeled Data
We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on
Question Answering
on CoQA
Syntactic Scaffolds for Semantic Structures
We introduce the syntactic scaffold, an approach to incorporating syntactic information into semantic tasks.
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features.
Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum
LSTMs were introduced to combat vanishing gradients in simple RNNs by augmenting them with gated additive recurrent connections.
Higher-order Coreference Resolution with Coarse-to-fine Inference
We introduce a fully differentiable approximation to higher-order inference for coreference resolution.
Ranked #14 on
Coreference Resolution
on OntoNotes
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Ranked #3 on
Citation Intent Classification
on ACL-ARC
(using extra training data)
Citation Intent Classification
Conversational Response Selection
+7
End-to-end Neural Coreference Resolution
We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector.
Ranked #15 on
Coreference Resolution
on CoNLL 2012
Deep Semantic Role Labeling: What Works and What's Next
We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.
Ranked #2 on
Predicate Detection
on CoNLL 2005
Recurrent Additive Networks
We introduce recurrent additive networks (RANs), a new gated RNN which is distinguished by the use of purely additive latent state updates.
Learning Recurrent Span Representations for Extractive Question Answering
In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network.
Ranked #43 on
Question Answering
on SQuAD1.1 dev
Global Neural CCG Parsing with Optimality Guarantees
We introduce the first global recursive neural parsing model with optimality guarantees during decoding.
