ConClue: Conditional Clue Extraction for Multiple Choice Question Answering

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.