A Persona-Based Neural Conversation Model

Li, Jiwei; Galley, Michel; Brockett, Chris; Spithourakis, Georgios P.; Gao, Jianfeng; Dolan, Bill

doi:10.18653/v1/p16-1094

Cited by 788 publications

(769 citation statements)

References 29 publications

Supporting

Mentioning

745

Contrasting

Unclassified

Order By: Relevance

“…Additional features such as persona information (Li et al, 2016b) and latent semantics Serban et al, 2017) have also been proven beneficial within this context.…”

Section: Related Workmentioning

confidence: 99%

Neural Response Generation via GAN with an Approximate Embedding Layer

Xu¹,

Liu²,

Wang³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

This paper presents a Generative Adversarial Network (GAN) to model singleturn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machinegenerated ones. In addition, the proposed method introduces an approximate embedding layer to solve the non-differentiable problem caused by the sampling-based output decoding procedure in the Seq2Seq generative model. The GAN setup provides an effective way to avoid noninformative responses (a.k.a "safe responses"), which are frequently observed in traditional neural response generators. The experimental results show that the proposed approach significantly outperforms existing neural response generation models in diversity metrics, with slight increases in relevance scores as well, when evaluated on both a Mandarin corpus and an English corpus.

show abstract

“…Additional features such as persona information (Li et al, 2016b) and latent semantics Serban et al, 2017) have also been proven beneficial within this context.…”

Section: Related Workmentioning

confidence: 99%

Neural Response Generation via GAN with an Approximate Embedding Layer

Xu¹,

Liu²,

Wang³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider the task of quote attribution for literary text: identifying the speaker for each quote. This task is important for developing realistic character-specific conversational models (Vinyals and Le, 2015;Li et al, 2016), analyzing discourse structure, and literary studies (Muzny et al, 2016). But identifying speakers can be difficult; authors often refer to the 1 Quotes in Literary text from 3 novels.…”

Section: Introductionmentioning

confidence: 99%

A Two-stage Sieve Approach for Quote Attribution

Muzny¹,

Fang²,

Chang³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

We present a deterministic sieve-based system for attributing quotations in literary text and a new dataset: QuoteLi3 1 . Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational literary studies, where it creates opportunities to analyze novels at scale rather than only a few at a time. We release QuoteLi3, which contains more than 6,000 annotations linking quotes to speaker mentions and quotes to speaker entities, and introduce a new algorithm for quote attribution. Our twostage algorithm first links quotes to mentions, then mentions to entities. Using two stages encapsulates difficult sub-problems and improves system performance. The modular design allows us to tune either for overall performance or for the high precision appropriate for many use cases. Our system achieves an average F-score of 87.5% across three novels, outperforming previous systems, and can be tuned for precision of 90.4% at a recall of 65.1%.

show abstract

“…In this seq2seq setup, moods and sentiments expressed in the past are replicated or reused, but these responses do not target particular topics and are not driven by a concrete user agenda. An exception is a recent work by Li et al (2016), exploring a persona-based conversational model, and Xu et al (2016) who encode loose structured knowledge to condition the generation on. These studies present a stepping stone towards full-fledge neural ONLG architectures with some control over the user characteristics.…”

Section: Related and Future Workmentioning

confidence: 99%

Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)

Cagan

Frank

Tsarfaty

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

View full text Add to dashboard Cite

Opinionated natural language generation (ONLG) is a new, challenging, NLG task in which we aim to automatically generate human-like, subjective, responses to opinionated articles online. We present a data-driven architecture for ONLG that generates subjective responses triggered by users' agendas, based on automatically acquired wide-coverage generative grammars. We compare three types of grammatical representations that we design for ONLG. The grammars interleave different layers of linguistic information, and are induced from a new, enriched dataset we developed. Our evaluation shows that generation with Relational-Realizational (Tsarfaty and Sima'an, 2008) inspired grammar gets better language model scores than lexicalized grammarsà la Collins (2003), and that the latter gets better humanevaluation scores. We also show that conditioning the generation on topic models makes generated responses more relevant to the document content.

show abstract

A Persona-Based Neural Conversation Model

Cited by 788 publications

References 29 publications

Neural Response Generation via GAN with an Approximate Embedding Layer

Neural Response Generation via GAN with an Approximate Embedding Layer

A Two-stage Sieve Approach for Quote Attribution

Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)

Contact Info

Product

Resources

About