@inproceedings{saeidi-etal-2018-interpretation,
title = "Interpretation of Natural Language Rules in Conversational Machine Reading",
author = {Saeidi, Marzieh and
Bartolo, Max and
Lewis, Patrick and
Singh, Sameer and
Rockt{\"a}schel, Tim and
Sheldon, Mike and
Bouchard, Guillaume and
Riedel, Sebastian},
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1233",
doi = "10.18653/v1/D18-1233",
pages = "2087--2097",
abstract = "Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader{'}s background knowledge. One example is the task of interpreting regulations to answer {``}Can I...?{''} or {``}Do I have to...?{''} questions such as {``}I am working in Canada. Do I have to carry on paying UK National Insurance?{''} after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as {``}How long have you been working abroad?{''} when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.",
}
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<abstract>Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader’s background knowledge. One example is the task of interpreting regulations to answer “Can I...?” or “Do I have to...?” questions such as “I am working in Canada. Do I have to carry on paying UK National Insurance?” after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as “How long have you been working abroad?” when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.</abstract>
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%0 Conference Proceedings
%T Interpretation of Natural Language Rules in Conversational Machine Reading
%A Saeidi, Marzieh
%A Bartolo, Max
%A Lewis, Patrick
%A Singh, Sameer
%A Rocktäschel, Tim
%A Sheldon, Mike
%A Bouchard, Guillaume
%A Riedel, Sebastian
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F saeidi-etal-2018-interpretation
%X Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader’s background knowledge. One example is the task of interpreting regulations to answer “Can I...?” or “Do I have to...?” questions such as “I am working in Canada. Do I have to carry on paying UK National Insurance?” after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as “How long have you been working abroad?” when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.
%R 10.18653/v1/D18-1233
%U https://aclanthology.org/D18-1233
%U https://doi.org/10.18653/v1/D18-1233
%P 2087-2097
Markdown (Informal)
[Interpretation of Natural Language Rules in Conversational Machine Reading](https://aclanthology.org/D18-1233) (Saeidi et al., EMNLP 2018)
ACL
- Marzieh Saeidi, Max Bartolo, Patrick Lewis, Sameer Singh, Tim Rocktäschel, Mike Sheldon, Guillaume Bouchard, and Sebastian Riedel. 2018. Interpretation of Natural Language Rules in Conversational Machine Reading. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2087–2097, Brussels, Belgium. Association for Computational Linguistics.