RECONSIDER : Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering (2021)

Abstract: State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking. We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training. RECONSIDER is trained on positive and negative examples extracted from high confidence MRC model predictions, and uses in-passage span annotations to perform span-focused re-ranking over a smaller candidate set. As a result, RECONSIDER learns to eliminate close false positives, achieving a new extractive state of the art on four QA tasks, with 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA. We will release all related data, models, and code.

## introduction open-domain question answering @xcite (qa) involves answering questions by extracting correct answer spans from a large corpus of passages, and is typically accomplished by a light-weight passage retrieval model followed by a heavier machine reading comprehension (mrc) model @xcite . the span selection components of mrc models are trained on distantly supervised positive examples (containing the answer string) together with heuristically chosen negative examples, typically from upstream retrieval models. this training scheme possibly explains empirical findings @xcite ) that while mrc models can confidently identify top-k answer candidates (high recall), they cannot effectively discriminate between top semantically similar false positive candidates (low accuracy). in this paper, we develop a general approach to make answer reranking successful for span-extraction tasks, even over large pretrained models, and improve the state of the art on four qa datasets. earlier work @xcite ) on opendomain qa have recognized the potential of answer re-ranking, which we continue to observe despite recent advances using large pre-trained models like bert @xcite . figure 1 shows the top-3 predictions of a bert-based sota model @xcite ) on a question from natural questions (nq) @xcite , "who was the head of the soviet union when it collapsed?" while all predictions are very relevant and refer to soviet union heads, mikhail gorbachev is correct and the rest are close false positives. our re-ranking model is trained using positive and negative examples extracted from high confidence mrc model predictions, and thus, learns to eliminate hard false positives. this can be viewed as a coarse-to-fine approach of training span selectors, with the base mrc model trained on heuristically chosen negatives and the re-ranker trained on finer, more subtle negatives. this contrasts with multi-task training approaches @xcite , whose re-scoring gains are limited by training on the same data, especially when coupled with large pre-trained models. our approach also scales to any number of ranked candidates, unlike previous concatenation based cross-passage re-ranking methods @xcite that do not transfer well to current length-bounded large pre-trained models. similar to mrc models, our re-ranking approach uses cross-attention between the question and a candidate passage @xcite . however, we now demarcate a specific candidate answer span in each passage, to assist the model to perform span-focused reasoning, in contrast to mrc models, which must reason across all spans. therefore, the re-ranker performs span ranking of carefully chosen candidates, rather than span selection like the mrc model. similar focused cross-attention methods have recently proved to be effective for entity linking @xcite tasks, although they annotate the query rather than the passage. we use our broadly applicable span-focused reranking approach on models from @xcite and achieve a new extractive state of the art on four qa datasets, including 45.5% on the opendomain setting of nq (real user queries, +1.6% on small models) and 61.1% on triviaqa @xcite (+2.5% on small models). to our knowledge, we are the first to successfully leverage re-ranking to improve over large pre-trained models on opendomain qa. ## background open-domain question answering (qa) aims to answer factoid questions from a large corpus of passages @xcite (such as wikipedia) in contrast with single passage mrc tasks @xcite . prior works use pipelined approaches, that first retrieve candidate passages and subsequently use a neural mrc model to extract answer spans @xcite , with further improvements using joint learning @xcite . recent successes involve improving retrieval, thereby increasing the coverage of passages fed into the mrc model @xcite . in this paper, we significantly improve mrc model performance by making re-ranking successful using span-focused re-ranking of its highly confident predictions. for open-domain qa, it is crucial to train mrc models to distinguish passage-span pairs containing the answer (positives) from those that do not (negatives). using negatives that appear as close false positives can produce more robust mrc models. however, prior work relies on upstream retrieval models to supply distantly supervised positives (contain answer string) and negatives @xcite , that are in-turn trained using heuristically chosen positives and negatives. our approach leverages positives and negatives from highly confident mrc predictions which are hard to classify, and thus, improve upon mrc model performance. jia and liang (2017) motivate recent work on answer verification for qa by showing that mrc models are easily confused by similar passages. wang et al. (2018b) use a weighted combination of three re-rankers and rescore a concatenation of all passages with a particular answer using a sequential model, while, @xcite develop a multi-task end-to-end answer scoring approach. although the main idea is to consider multiple passage-span candidates collectively, such approaches either used concatenation, which is prohibitively expensive to couple with lengthrestricted models like bert, or are trained on the same data without variations only to realize marginal gains. @xcite use answer verification to predict the unanswerability of a question-passage pair for traditional mrc tasks. to our knowledge, our work is the first to (i) successfully demonstrate a re-ranking approach that significantly improves over large pre-trained models @xcite in an open domain setting, and (ii) use annotated top model predictions as harder negatives to train more robust models for qa. ## model we assume an extractive mrc model m coupled with a passage retrieval model, that given a question q and a passage corpus p, produces a list of n passage and span pairs, {(p j , s j )} n @xmath0, p @xmath1, ranked by the likelihood of s j answering q. note that {p j } n @xmath2. in this section, we develop a span-focused re-ranking model r, that learns a distribution p, over top-k (p j , s j ) pairs 1 ≤ @xmath3, given question q. essentially, model r first scores every (q, p j , s j ) triple using scoring function r, and then normalizes over these scores to produce p: specifically, if e(q, p j , s j ) ∈ r h is a dense representation of (q, p j , s j ), r is defined as: r(q, p j , s j ) = w t e(q, p j , s j ), ## baseline model m we use the state-of-the-art models of @xcite which consists of 1) a dense passage retriever, and 2) a span extractive bert reader, as our model m. the retriever uses a passage encoder f p and a question encoder f q to represent all passages and questions as dense vectors in the same space. during inference, it retrieves top-100 passages similar to question q based on their inner product, and passes them on to the mrc reader. the mrc reader is an extension of model r of section 3, to perform span extraction. we briefly describe it but @xcite has complete details. its input is a question q together with positive and negative passages p j from its retrieval model. (q, p j ) tuples are encoded as before (enc(q, p j ) = q [sep] p j ), but without spans being marked (as spans are unavailable). a distribution over passages p s is computed as before using scoring function r and context encoder e. in addition, a start-span probability, p st (t i |q, p j ) and an end-span probability, p e (t i |q, p j ) is computed for every token t i in enc(q, p j ). the model is trained to maximize the likelihood of p s (p j ) × p st (s|q, p j ) × p e (t|q, p j ) for each correct answer span (s, t) in p j , and outputs the top-k scoring passage-span pairs during inference. triviaqa @xcite consists of questions collected from trivia and quiz-league websites; we take questions in an unfiltered setting and discard the provided web snippets. webquestions (webq) @xcite is a collection of questions extracted from the google suggest api, with answers being freebase entities. curatedtrec @xcite contains curated questions from trec qa track. ## conclusion we use a synergistic combination of two techniques viz. retraining with harder negatives, and, spanfocused cross attention, to make re-ranking successful for span-extractive tasks over large pretrained models. this method achieves sota extractive results on four open domain qa datasets, also outperforming recent generative pre-training approaches.