An Information Retrieval Approach to Short Text Conversation

Ji, Zongcheng; Lu, Zhengdong; Li, Hang

doi:10.48550/arxiv.1408.6988

Cited by 35 publications

(50 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the key element is the query-response operation. In general, this problem has been formulated as a search problem and uses IR techniques for task completion [171]. Retrieval-based methods usually employ either Single-turn Response Matching or Multi-turn Response Matching.…”

Section: H Dialogue Systemsmentioning

confidence: 99%

Natural Language Processing Advancements By Deep Learning: A Survey

Torfi,

Shirvani,

Keneshloo

et al. 2020

Preprint

View full text Add to dashboard Cite

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications, and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

show abstract

Section: H Dialogue Systemsmentioning

confidence: 99%

Natural Language Processing Advancements By Deep Learning: A Survey

Torfi,

Shirvani,

Keneshloo

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The retrieve-and-generation approaches are developed for many tasks, including dialogue generation (Weston et al, 2018;, language modeling , code generation ) and text summarization (Rush et al, 2015;Cao et al, 2018a;Peng et al, 2019). Ji et al (2014) and focuses on prototype ranking in the retrieval-based model but they do not edit these retrieved prototypes. Re3Sum (Cao et al, 2018b) is an LSTM-based model developed under the retrieve-and-generation framework that retrieves multiple headlines and pick the single best retrieved headline, then edit.…”

Section: Retrieve-and-generationmentioning

confidence: 99%

An Enhanced Knowledge Injection Model for Commonsense Generation

Fan

Gong

Wei

et al. 2020

Preprint

View full text Add to dashboard Cite

Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules, namely position indicator and scaling module, into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, and experimental results show that our method significantly improves the performance on all the metrics.

show abstract

“…In general, chat bots are implemented either by generative methods or retrieval-based methods. Generative models are able to generate more proper responses that could have never appeared in the corpus, while retrieval-based models enjoy the advantage of informative and fluent responses [30], because they select a proper response for the current conversation from a repository with response selection algorithms. In the following sections, we will first dive into the neural generative models, one of the most popular research topics in recent years, and discuss their drawbacks and possible improvements.…”

Section: Contextmentioning

confidence: 99%

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Chen,

Liu,

Yin

et al. 2017

Preprint

View full text Add to dashboard Cite

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as computer vision, natural language processing, and recommender systems. For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting. In this article, we give an overview to these recent advances on dialogue systems from various perspectives and discuss some possible research directions. In particular, we generally divide existing dialogue systems into task-oriented and nontask-oriented models, then detail how deep learning techniques help them with representative algorithms and finally discuss some appealing research directions that can bring the dialogue system research into a new frontier.

show abstract

An Information Retrieval Approach to Short Text Conversation

Cited by 35 publications

References 12 publications

Natural Language Processing Advancements By Deep Learning: A Survey

Natural Language Processing Advancements By Deep Learning: A Survey

An Enhanced Knowledge Injection Model for Commonsense Generation

A Survey on Dialogue Systems: Recent Advances and New Frontiers

Contact Info

Product

Resources

About