64 citations · 156 across the 18 of their papers we have counts for
21 papers
Structured Summarization: Unified Text Segmentation and Segment Labeling as a Generation Task
Hakan Inan, Rashi Rungta, Yashar Mehdad
Text segmentation aims to divide text into contiguous, semantically coherent segments, while segment labeling deals with producing labels for each segment. Past work has shown succ…
Syntax-augmented Multilingual BERT for Cross-lingual Transfer
Wasi Uddin Ahmad, Haoran Li, Kai-Wei Chang +1
In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning…
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi +4
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.…
EASE: Extractive-Abstractive Summarization with Explanations
Haoran Li, Arash Einolghozati, Srinivasan Iyer +4
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. To achieve…
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li +3
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Kushal Lakhotia, Bhargavi Paranjape, Asish Ghoshal +3
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tas…
Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan +3
While research on explaining predictions of open-domain QA systems (ODQA) to users is gaining momentum, most works have failed to evaluate the extent to which explanations improve…
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Barlas Oguz, Xilun Chen, Vladimir Karpukhin +6
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior…
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
Srinivasan Iyer, Sewon Min, Yashar Mehdad +1
State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive exa…
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
Xilun Chen, Asish Ghoshal, Yashar Mehdad +2
Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user's intents (set reminder, play music, etc.). Recent adv…
Efficient One-Pass End-to-End Entity Linking for Questions
Belinda Z. Li, Sewon Min, Srinivasan Iyer +2
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and G…
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Alexander R. Fabbri, Simeng Han, Haoyuan Li +5
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typicall…
Conversational Semantic Parsing
Armen Aghajanyan, Jean Maillard, Akshat Shrivastava +8
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the repr…
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Wenhan Xiong, Xiang Lorraine Li, Srini Iyer +8
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datas…
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Haoran Li, Abhinav Arora, Shuohui Chen +3
Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets su…
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Sheng Chen, Akshay Soni, Aasish Pappu +1
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit sev…
Online Article Ranking as a Constrained, Dynamic, Multi-Objective Optimization Problem
Jeya Balaji Balasubramanian, Akshay Soni, Yashar Mehdad +1
The content ranking problem in a social news website, is typically a function that maximizes a scalar metric of interest like dwell-time. However, like in most real-world applicati…
Rank-to-engage: New Listwise Approaches to Maximize Engagement
Swayambhoo Jain, Akshay Soni, Nikolay Laptev +1
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is c…
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning
Akshay Soni, Yashar Mehdad
The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that onl…
Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings
Vivek Kulkarni, Yashar Mehdad, Troy Chevalier
Content on the Internet is heterogeneous and arises from various domains like News, Entertainment, Finance and Technology. Understanding such content requires identifying named ent…