Open-domain question answering answers a
question based on evidence retrieved from
a large corpus. State-of-the-art neural approaches require intermediate evidence annotations for training. However, such intermediate annotations are expensive, and methods that rely on them cannot transfer to the
more common setting, where only question–
answer pairs are available. This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost. We introduce a novel approach
(DISTDR) that iteratively improves over a
weak retriever by alternately finding evidence
from the up-to-date model and encouraging
the model to learn the most likely evidence.
Without using any evidence labels, DISTDR
is on par with fully-supervised state-of-theart methods on both multi-hop and singlehop QA benchmarks. Our analysis confirms
that DISTDR finds more accurate evidence
over iterations, which leads to model improvements. The code is available at https://
github.com/henryzhao5852/DistDR.
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Weakly-Supervised Open-Retrieval Conversational Question Answering
Recent work on Question Answering (QA) and Conversational QA (ConvQA) emphasizes the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval setting typically assumes that each question is answerable by a single span of text within a particular passage (a span answer). The supervision signal is thus derived from whether or not the system can recover an exact match of this ground-truth answer span from the retrieved passages. This method is referred to as span-match weak supervision. However, information-seeking conversations are challenging for this span-match method since long answers, especially freeform answers, are not necessarily strict spans of any passage. Therefore, we introduce a learned weak supervision approach that can identify a paraphrased span of the known answer in a passage. Our experiments on QuAC and CoQA datasets show that although a span-match weak supervisor can handle conversations with span answers, it is not sufficient for freeform answers generated by people. We further demonstrate that our method is more flexible since it can handle both span answers and freeform answers. In particular, our method outperforms the span-match method on conversations with freeform answers, and it can be more powerful when combined with the span-match method. We also conduct in-depth analyses to show more insights on open-retrieval ConvQA under a weak supervision setting.
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- Award ID(s):
- 1715095
- PAR ID:
- 10277182
- Date Published:
- Journal Name:
- Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021)
- Page Range / eLocation ID:
- 529-543
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-72113-8_35
