TY - GEN
T1 - The negochat corpus of human-agent negotiation dialogues
AU - Konovalov, Vasily
AU - Artstein, Ron
AU - Melamud, Oren
AU - Dagan, Ido
N1 - Funding Information: We thank Sarit Kraus, Avi Rosenfeld, Erel Segal-Halevi, Osnat Drein and Inon Zuckerman for their assistance and contribution. This work was partly supported by ERC Grant #267523.
PY - 2016
Y1 - 2016
N2 - Annotated in-domain corpora are crucial to the successful development of dialogue systems of automated agents, and in particular for developing natural language understanding (NLU) components of such systems. Unfortunately, such important resources are scarce. In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain. The corpus was collected using Amazon Mechanical Turk following the 'Wizard-Of-Oz' approach, where a 'wizard' human translates the participants' natural language utterances in real time into a semantic language. Once dialogue collection was completed, utterances were annotated with intent labels by two independent annotators, achieving high inter-annotator agreement. Our initial experiments with an SVM classifier show that automatically inferring such labels from the utterances is far from trivial. We make our corpus publicly available to serve as an aid in the development of dialogue systems for negotiation agents, and suggest that analogous corpora can be created following our methodology and using our available source code. To the best of our knowledge this is the first publicly available negotiation dialogue corpus.
AB - Annotated in-domain corpora are crucial to the successful development of dialogue systems of automated agents, and in particular for developing natural language understanding (NLU) components of such systems. Unfortunately, such important resources are scarce. In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain. The corpus was collected using Amazon Mechanical Turk following the 'Wizard-Of-Oz' approach, where a 'wizard' human translates the participants' natural language utterances in real time into a semantic language. Once dialogue collection was completed, utterances were annotated with intent labels by two independent annotators, achieving high inter-annotator agreement. Our initial experiments with an SVM classifier show that automatically inferring such labels from the utterances is far from trivial. We make our corpus publicly available to serve as an aid in the development of dialogue systems for negotiation agents, and suggest that analogous corpora can be created following our methodology and using our available source code. To the best of our knowledge this is the first publicly available negotiation dialogue corpus.
KW - Crowdsourcing
KW - Dialogue systems
KW - Negotiation corpora
UR - https://www.scopus.com/pages/publications/85037129540
M3 - Conference contribution
T3 - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
SP - 3141
EP - 3145
BT - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Grobelnik, Marko
A2 - Odijk, Jan
A2 - Piperidis, Stelios
A2 - Maegaard, Bente
A2 - Mariani, Joseph
T2 - 10th International Conference on Language Resources and Evaluation, LREC 2016
Y2 - 23 May 2016 through 28 May 2016
ER -