Information Seeking

39 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

aliannejadi/ClariQ 23 Sep 2020

The main aim of the conversational systems is to return an appropriate answer in response to the user requests.

XOR QA: Cross-lingual Open-Retrieval Question Answering

AkariAsai/XORQA NAACL 2021

Multilingual question answering tasks typically assume answers exist in the same language as the question.

doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset

IBM/multidoc2dial EMNLP 2020

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.

Deep Reinforcement Learning for Multi-Domain Dialogue Systems

cuayahuitl/SimpleDS 26 Nov 2016

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems.

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

yangliuy/NeuralResponseRanking 1 May 2018

Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

User Intent Prediction in Information-seeking Conversations

prdwb/UserIntentPrediction 11 Jan 2019

Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations.

World Discovery Models

subinlab/model_based_rl_paper 20 Feb 2019

As humans we are driven by a strong desire for seeking novelty in our world.

Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

aliannejadi/qulac 15 Jul 2019

In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems.

Interactive Machine Comprehension with Information Seeking Agents

xingdi-eric-yuan/imrc_public ACL 2020

Existing machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA).

Interactive Language Learning by Question Answering

xingdi-eric-yuan/qait_public IJCNLP 2019

In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions.