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Found 29 papers, 4 papers with code
A Joint Model for Answer Sentence Ranking and Answer Extraction
Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i. e., as independent tasks.
Query-Focused Opinion Summarization for User-Generated Content
We present a submodular function-based framework for query-focused opinion summarization.
A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization
We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization.
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
CFO: A Framework for Building Production NLP Systems
This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.
Cross-Task Knowledge Transfer for Query-Based Text Summarization
We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
On the Importance of Diversity in Question Generation for QA
Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA).
Multi-Stage Pre-training for Low-Resource Domain Adaptation
Transfer learning techniques are particularly useful in NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain.
Improved Synthetic Training for Reading Comprehension
Automatically generated synthetic training examples have been shown to improve performance in machine reading comprehension (MRC).
Answer Span Correction in Machine Reading Comprehension
Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair.
Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers
We present scalable Universal Dependency (UD) treebank synthesis techniques that exploit advances in language representation modeling which leverage vast amounts of unlabeled general-purpose multilingual text.
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training
End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages.
Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems.
Synthetic Target Domain Supervision for Open Retrieval QA
Neural passage retrieval is a new and promising approach in open retrieval question answering.
Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection
Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout the chapter.
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
Taxonomy Expansion for Named Entity Recognition
We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
Intent classification (IC) plays an important role in task-oriented dialogue systems.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines.
