Prediction of Nash Bargaining Solution in Negotiation Dialogue

Iwasa, Kosui; Fujita, Katsuhide

doi:10.1007/978-3-319-97304-3_60

Cited by 2 publications

(4 citation statements)

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“…NLP for Human-human Negotiation Support Automated negotiation has gained a great deal attention, but there have been only a few studies conducted on support for human-human negotiation in natural language: Iwasa and Fujita (2018) have proposed a GRU-based model to suggest a draft agreement that maximizes the sum of utilities based on the estimated weights of all items in the DN dataset. Zhou et al (2019) proposed a dynamic negotiation coaching method in the setting of CB dataset that provides useful recommendations to sellers, resulting in increased profits.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, several studies have succeeded in modeling a negotiating agent in natural language that can control both text generation and reasoning in the context of goaloriented dialogue systems, and such agents have produced better performance than human players in some cases (Lewis et al, 2017;He et al, 2018;Cheng et al, 2019). Further, support for humanhuman negotiation in natural language has also been tackled, involving negotiation corpora developed for goal-oriented dialogue systems, such as a Nash bargaining solution estimation (Iwasa and Fujita, 2018), real-time negotiation coaching (Zhou et al, 2019), and negotiation breakdown detection (Yamaguchi and Fujita, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Although they have recently attracted additional attention, there are only few negotiation corpora, as the most recent follow-up studies (Iwasa and Fujita, 2018;Cheng et al, 2019;Zhou et al, 2019;Yamaguchi and Fujita, 2020) have only utilized either the DEALORNODEAL (DN) (Lewis et al, 2017), CRAIGSLISTBARGAIN (CB) (He et al, 2018) datasets or both. Moreover, most existing corpora have simplified negotiation settings; for example, the DN dataset handles the negotiation of item division between humans with 22.5 possible solutions per dialogue and uses a standard linear additive utility function (Keeney and Raiffa, 1993;Raiffa et al, 2002) for scoring.…”

Section: Introductionmentioning

confidence: 99%

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Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Yamaguchi

Iwasa²,

Fujita

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Thanks to the success of goal-oriented negotiation dialogue systems, studies of negotiation dialogue have gained momentum in terms of both human-human negotiation support and dialogue systems. However, the field suffers from a paucity of available negotiation corpora, which hinders further development and makes it difficult to test new methodologies in novel negotiation settings. Here, we share a human-human negotiation dialogue dataset in a job interview scenario that features increased complexities in terms of the number of possible solutions and a utility function. We test the proposed corpus using a breakdown detection task for human-human negotiation support. We also introduce a dialogue act-based breakdown detection method, focusing on dialogue flow that is applicable to various corpora. Our results show that our proposed method features comparable detection performance to text-based approaches in existing corpora and better results in the proposed dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Yamaguchi

Iwasa²,

Fujita

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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“…While previous efforts aimed to develop an agent that can negotiate in natural language, few studies have been conducted on supporting human-human negotiation. Only Iwasa and Fujita (2018) introduced an end-to-end model that can estimate the preferences of each agent trained on the dataset reported by Lewis et al (2017) and tried to find the Nash bargaining solution for each dialogue.…”

Section: Introductionmentioning

confidence: 99%

Breakdown Detection in Negotiation Dialogues (Student Abstract)

Yamaguchi

Fujita

2020

AAAI

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In human-human negotiation, reaching a rational agreement can be difficult, and unfortunately, the negotiations sometimes break down because of conflicts of interests. If artificial intelligence can play a role in assisting with human-human negotiation, it can assist in avoiding negotiation breakdown, leading to a rational agreement. Therefore, this study focuses on end-to-end tasks for predicting the outcome of a negotiation dialogue in natural language. Our task is modeled using a gated recurrent unit and a pre-trained language model: BERT as the baseline. Experimental results demonstrate that the proposed tasks are feasible on two negotiation dialogue datasets, and that signs of a breakdown can be detected in the early stages using the baselines even if the models are used in a partial dialogue history.

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Prediction of Nash Bargaining Solution in Negotiation Dialogue

Cited by 2 publications

References 15 publications

Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Breakdown Detection in Negotiation Dialogues (Student Abstract)

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