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Vocabulary alignment for collaborative agents: a study with real-world multilingual how-to instructions

Vocabulary alignment for collaborative agents: a study with real-world multilingual how-to instructions
Vocabulary alignment for collaborative agents: a study with real-world multilingual how-to instructions

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels. The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.

159-165
International Joint Conferences on Artificial Intelligence
Chocron, Paula
590f3d36-5407-423e-9e14-3f0f17badc1a
Pareti, Paolo
c4337eaa-f206-4639-afd2-3bcbfe734cdb
Chocron, Paula
590f3d36-5407-423e-9e14-3f0f17badc1a
Pareti, Paolo
c4337eaa-f206-4639-afd2-3bcbfe734cdb

Chocron, Paula and Pareti, Paolo (2018) Vocabulary alignment for collaborative agents: a study with real-world multilingual how-to instructions. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. vol. 2018-July, International Joint Conferences on Artificial Intelligence. pp. 159-165 . (doi:10.24963/ijcai.2018/22).

Record type: Conference or Workshop Item (Paper)

Abstract

Collaboration between heterogeneous agents typically requires the ability to communicate meaningfully. This can be challenging in open environments where participants may use different languages. Previous work proposed a technique to infer alignments between different vocabularies that uses only information about the tasks being executed, without any external resource. Until now, this approach has only been evaluated with artificially created data. We adapt this technique to protocols written by humans in natural language, which we extract from instructional webpages. In doing so, we show how to take into account challenges that arise when working with natural language labels. The quality of the alignments obtained with our technique is evaluated in terms of their effectiveness in enabling successful collaborations, using a translation dictionary as a baseline. We show how our technique outperforms the dictionary when used to interact.

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More information

Published date: 2018
Venue - Dates: 27th International Joint Conference on Artificial Intelligence, , Stockholm, Sweden, 2018-07-12 - 2018-07-18

Identifiers

Local EPrints ID: 426542
URI: http://eprints.soton.ac.uk/id/eprint/426542
PURE UUID: ed5074c0-c41d-4386-88d5-18f4828250f0

Catalogue record

Date deposited: 30 Nov 2018 17:30
Last modified: 25 Nov 2021 19:49

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Contributors

Author: Paula Chocron
Author: Paolo Pareti

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