Browse > Natural Language Processing > Question Answering

Question Answering

270 papers with code · Natural Language Processing

Question answering is the task of answering a question.

State-of-the-art leaderboards

Trend Dataset Best Method Paper title Paper Code Compare

Latest papers without code

Hyperbolic Attention Networks

ICLR 2019 Caglar Gulcehre et al

Recent approaches have successfully demonstrated the benefits of learning the parameters of shallow networks in hyperbolic space.

MACHINE TRANSLATION QUESTION ANSWERING VISUAL QUESTION ANSWERING

01 May 2019

Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation

ICLR 2019 Rodrigo Nogueira et al

We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering.

QUESTION ANSWERING

01 May 2019

NLProlog: Reasoning with Weak Unification for Natural Language Question Answering

ICLR 2019 Leon Weber et al

Currently, most work in natural language processing focuses on neural networks which learn distributed representations of words and their composition, thereby performing well in the presence of large linguistic variability.

QUESTION ANSWERING

01 May 2019

The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

ICLR 2019 Jiayuan Mao et al

To bridge the learning of two modules, we use a neural-symbolic reasoning module that executes these programs on the latent scene representation.

QUESTION ANSWERING SEMANTIC PARSING VISUAL QUESTION ANSWERING

01 May 2019

Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering

ICLR 2019 Ramakrishna Vedantam et al

We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs.

DECISION MAKING QUESTION ANSWERING VISUAL QUESTION ANSWERING

01 May 2019

Learning to Decompose Compound Questions with Reinforcement Learning

ICLR 2019 Haihong Yang et al

Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.

QUESTION ANSWERING

01 May 2019

Weakly-supervised Knowledge Graph Alignment with Adversarial Learning

ICLR 2019 Meng Qu et al

We propose an unsupervised framework based on adversarial training, which is able to map the entities and relations in a source knowledge graph to those in a target knowledge graph.

KNOWLEDGE GRAPHS QUESTION ANSWERING

01 May 2019

Scalable Neural Theorem Proving on Knowledge Bases and Natural Language

ICLR 2019 Pasquale Minervini et al

Reasoning over text and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.

AUTOMATED THEOREM PROVING LINK PREDICTION QUESTION ANSWERING READING COMPREHENSION

01 May 2019

Cross-Task Knowledge Transfer for Visually-Grounded Navigation

ICLR 2019 Devendra Singh Chaplot et al

Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for two different tasks: learning to follow navigational instructions and embodied question answering.

EMBODIED QUESTION ANSWERING QUESTION ANSWERING TRANSFER LEARNING VISUAL NAVIGATION

01 May 2019

Efficient Codebook and Factorization for Second Order Representation Learning

ICLR 2019 Pierre jacob et al

This paper addresses these two points by extending factorization schemes to codebook strategies, allowing compact representations with the same dimensionality as first order representations, but with second order performances.

IMAGE RETRIEVAL OBJECT RECOGNITION QUESTION ANSWERING REPRESENTATION LEARNING VISUAL QUESTION ANSWERING

01 May 2019