TY - CHAP
T1 - ConClue
T2 - conditional clue extraction for multiple choice question answering
AU - Yang, Wangli
AU - Yang, Jie
AU - Li, Wanqing
AU - Guo, Yi
PY - 2024
Y1 - 2024
N2 - The task of Multiple Choice Question Answering (MCQA) aims to identify the correct answer from a set of candidates, given a background passage and an associated question. Considerable research efforts have been dedicated to addressing this task, leveraging a diversity of semantic matching techniques to estimate the alignment among the answer, passage, and question. However, key challenges arise as not all sentences from the passage contribute to the question answering, while only a few supporting sentences (clues) are useful. Existing clue extraction methods suffer from inefficiencies in identifying supporting sentences, relying on resource-intensive algorithms, pseudo labels, or overlooking the semantic coherence of the original passage. Addressing this gap, this paper introduces a novel extraction approach, termed Conditional Clue extractor (ConClue), for MCQA. ConClue leverages the principles of Conditional Optimal Transport to effectively identify clues by transporting the semantic meaning of one or several words (from the original passage) to selected words (within identified clues), under the prior condition of the question and answer. Empirical studies on several competitive benchmarks consistently demonstrate the superiority of our proposed method over different traditional approaches, with a substantial average improvement of 1.1-2.5 absolute percentage points in answering accuracy.
AB - The task of Multiple Choice Question Answering (MCQA) aims to identify the correct answer from a set of candidates, given a background passage and an associated question. Considerable research efforts have been dedicated to addressing this task, leveraging a diversity of semantic matching techniques to estimate the alignment among the answer, passage, and question. However, key challenges arise as not all sentences from the passage contribute to the question answering, while only a few supporting sentences (clues) are useful. Existing clue extraction methods suffer from inefficiencies in identifying supporting sentences, relying on resource-intensive algorithms, pseudo labels, or overlooking the semantic coherence of the original passage. Addressing this gap, this paper introduces a novel extraction approach, termed Conditional Clue extractor (ConClue), for MCQA. ConClue leverages the principles of Conditional Optimal Transport to effectively identify clues by transporting the semantic meaning of one or several words (from the original passage) to selected words (within identified clues), under the prior condition of the question and answer. Empirical studies on several competitive benchmarks consistently demonstrate the superiority of our proposed method over different traditional approaches, with a substantial average improvement of 1.1-2.5 absolute percentage points in answering accuracy.
KW - Clue Extraction
KW - Machine Reading Comprehension
KW - Multiple Choice Question Answering
KW - Optimal Transport
UR - http://www.scopus.com/inward/record.url?scp=85204536694&partnerID=8YFLogxK
UR - https://ezproxy.uws.edu.au/login?url=https://doi.org/10.1007/978-3-031-70552-6_11
U2 - 10.1007/978-3-031-70552-6_11
DO - 10.1007/978-3-031-70552-6_11
M3 - Chapter
AN - SCOPUS:85204536694
SN - 9783031705519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 198
BT - Document Analysis and Recognition, ICDAR 2024, 18th International Conference, Athens, Greece, August 30 - September 4, 2024, Proceedings, Part VI
A2 - Barney Smith, Elisa H.
A2 - Liwicki, Marcus
A2 - Peng, Liangrui
PB - Springer
CY - Switzerland
ER -
