mono QA : Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering (2022)

Abstract: To address the Conversational Question Answering (ORConvQA) task, previous work has considered an effective three-stage architecture, consisting of a retriever, a reranker, and a reader to extract the answers. In order to effectively answer the users’ questions, a number of existing approaches have applied multi-task learning, such that the same model is shared between the reranker and the reader. Such approaches also typically tackle reranking and reading as classification tasks. On the other hand, recent text generation models, such as monoT5 and UnifiedQA, have been shown to respectively yield impressive performances in passage reranking and reading. However, no prior work has combined monoT5 and UnifiedQA to share a single text generation model that directly extracts the answers for the users instead of predicting the start/end positions in a retrieved passage. In this paper, we investigate the use of Multi-Task Learning (MTL) to improve performance on the ORConvQA task by sharing the reranker and reader’s learned structure in a generative model. In particular, we propose monoQA, which uses a text generation model with multi-task learning for both the reranker and reader. Our model, which is based on the T5 text generation model, is fine-tuned simultaneously for both reranking (in order to improve the precision of the top retrieved passages) and extracting the answer. Our results on the OR-QuAC and OR-CoQA datasets demonstrate the effectiveness of our proposed model, which significantly outperforms existing strong baselines with improvements ranging from +12.31% to +19.51% in MAP and from +5.70% to +23.34% in F1 on all used test sets.

## introduction research in conversational search and conversational question answering (convqa) is of increasing interest. the convqa task @xcite consists in understanding the question based on a given conversational history, and extracting an answer from a given passage. this task is an extractive type of qa, meaning that the answer takes the form of a span in the provided passage, and can be successfully tackled by employing an extractive or generative reader. extractive reader models @xcite ) are typically employed, where the goal is to classify the start and end positions of the answer span in the given passage. in contrast, generative readers @xcite @xcite have demonstrated impressive results on the extractive qa tasks, where the goal is to generate tokens that are a subset of a passage. recently, there has been more focus on retrieval as part of the convqa pipeline, known as open-retrieval conversational question answering (orconvqa). in this setting, the orconvqa system needs to apply the convqa model upon passages retrieved from a large collection, given a question, before actually extracting the answer. to address the orconvqa task, prior works @xcite @xcite ) have adopted a three-stage architecture, including a retriever, a reranker, and a reader to extract the answers. first, the retriever retrieves the top k relevant passages from the collection based on a question and its conversation history. the reranker and the reader then respectively rerank and identify an answer in the top k passages. we also adopt this threestage architecture in our proposed model. however, in order to investigate the effectiveness of the cross-encoder reranker, we consider a two-stage pipeline including a retriever and a reader, as a baseline for comparison with our system. for the retriever, existing works @xcite @xcite @xcite have focused on using bi-encoder dense retrieval (a question encoder and a passage encoder), which applies neural contextual language models, such as albert or bert, for encoding the question and passage into low-dimensional vectors and computing their relevance scores. for example, @xcite proposed convdr, which encodes the question and its history in a dense vector learned with a teacher-student model to mimic a dense representation of the manually rewritten question. convdr has also been shown to outperform other retriever models for conversational search such as sparse bm25, and bi-encoders using albert @xcite or bert @xcite . due to the good effectiveness of bi-encoder dense retrievers for passage retrieval, we adapt this type of retrieval model as our retriever. we also consider other recent existing bi-encoder passage retrievers such as tct-colbert @xcite and cqe @xcite as baseline passage retrievers. recently, multi-task learning (mtl), which is a method of learning multiple different but related tasks at the same time, has become a popular approach for tackling several tasks using a uniform model @xcite . for instance, mtl has been employed in order to efficiently answer the questions posed by the users @xcite . in this manner, the network structure is shared between the reranker and the reader. doing this, existing works @xcite also typically approach reranking and extractive reading as classification tasks, with two fully-connected layers (one for the reranker and reader, respectively) added to find an answer span for the retrieved passages (start/end positions) as well as to predict the relevance score of the question to the passage as shown in figure 1(a) . in this paper, we use the multi-task learning of the reranker and the extractive reader as our strongest baseline. on the other hand, @xcite proposed monot5, a text generation model, which was fine-tuned to generate the tokens "true" or "false" depending on whether the document is relevant or not to the query. indeed, the monot5 model has been shown to outperform bert-based models in passage reranking @xcite . in addition, many studies @xcite @xcite have focused on developing a generative reader which is fine-tuned as a text generation model to extract the answer from the passage. in particular, @xcite introduced the unifiedqa model, which has been shown to yield impressive performances on many extractive qa datasets. however, we show that compared to using monot5 and unifiedqa separately, a joint learning can enhance the learning efficiency and prediction accuracy of a model for the orconvqa task, since by sharing the learning model the reranker and reader can simultaneously predict the answer and reranking score. indeed, a joint learning by sharing a single model trained using mtl reduces the memory needs and speeds up inference @xcite . in addition, we combine the effective monot5 (to rerank the retrieved passages) and unifiedqa (to extract the answer from the highest scored passage) models into a strong baseline. to the best of our knowledge, no prior work has combined monot5 and unifiedqa by sharing a single text generation model, in order to directly extract the answers instead of predicting the start/end positions in a retrieved passage. in summary, in this work, we investigate the use of multi-task learning (mtl) to improve performance on the orconvqa task by sharing the reranker and reader's learned structure. we propose monoqa, which uses a text generation model with multi-task learning for both the reranker and reader. our model, which is based on the t5 @xcite text generation model, is fine-tuned simultaneously for both reranking (in order to improve the precision of the top retrieved passages) and extracting the answer. unlike previous work, monoqa makes predictions by generating the first token for the passage reranking task, followed by the other tokens for the answer extraction task, as illustrated in figure 1(b) . our contributions are summarised as follows: (1) we leverage multi-task learning with a text generation model by sharing the reranker and reader's learned structure to effectively addresses the orconvqa task; (2) using two different orconvqa datasets, we compare our model to two strong baselines from the literature, and show that our mtl reranker and generative reader approach yields the best f1, recall, mrr, figure 2 : an example dialog and relevant passages from the orconvqa dataset @xcite . and map performance improvements over the strongest baseline with statistically significant improvements ranging from +5.70% to +23.34%; (3) the proposed mtl model combining the reranker and generative reader significantly outperforms and is twice as fast for inference than the individual application of the monot5 and unifiedqa models for reranking and extracting the answer. ## a three-stage pipeline for a orconvqa system we first define the orconvqa task in section 2.1. an overview of the proposed mtl text generation model follows in section 2.2. then, we explain how to fine-tune the monoqa model in section 2.3. ## experimental setup our experiments address the three following research questions: rq1: which model to use for initialising monoqa, namely which of: monot5, unifiedqa, and t5-base, lead to the best performance of monoqa on the orconvqa task? rq2: how does monoqa compare to other existing baselines, namely: (1). the orconvqa system proposed by @xcite ; (2). the orconvqa system proposed by @xcite but using convdr as a retriever; and (3). using convdr as a retriever, monot5 as a reranker, and unifiedqa as a reader? rq3: how does our proposed system, which is a three-stage pipeline (retriever, reranker, and reader), compare to the two-stage pipeline baselines? ## experimental results we now address rqs 1-3 (see section 3) and conclude with an efficiency analysis. ## related work in the following, we discuss related work and position our contribution in relation to conversational question answering and multi-task learning. conversational question answering: this is a conversational search task, where the system needs to correctly interpret a question in the context of an ongoing conversation. most research on conversational qa focuses on conversational response ranking tasks @xcite @xcite and extractive qa tasks @xcite @xcite . @xcite were first to define the task of open-retrieval conversational question answering (or-convqa), where the system is required to learn to retrieve top relevant passages from a large collection before extracting answers from the passages. to address the orconvqa task, @xcite proposed a three-stage pipeline: (1) a retriever, (2) a reranker, and (3) a reader. later, @xcite introduced a learned weakly-supervised training approach to address the problem of accessing gold passages during the training of the model on the or-coqa dataset in @xcite . one particular problem is coarse ranking, where the reranker of @xcite only takes the top-ranked passages as input. this makes the whole pipeline suffers from this coarse ranking, especially for situations when the golden passages are not retrieved in the top-ranked passages. @xcite addressed this issue by adding a post-ranker module that can push more relevant passages to the reader. however, these works typically approach reranking and reading as classification tasks to find an answer span for retrieved passages (start/end positions). instead, we use a text generation model with multi-task learning for both the reranker and reader in order to directly extract the answers for the users instead of predicting the start/end positions in a retrieved passage. multi-task learning (mtl) in conversational qa: mtl methods have recently been effectively implemented in existing conversational qa works @xcite @xcite @xcite . however, all of the tasks in these works leverage mtl by sharing the network structure between an extractive reader and its auxiliary tasks, which are typically classification tasks, such as a yes/no question prediction or a follow-up question prediction. for example, some existing works @xcite have applied mtl on reranking and answer reading by adding two fully-connected layers (one for the reranker and reader, respectively) to find an answer span for the retrieved passages (start/end positions). instead, in this paper, we leverage mtl on reranking and answer extraction by sharing a single text generation model in order to directly extract the answers instead of predicting the start/end positions in a retrieved passage. on the other hand, @xcite recently adopted an mtl approach that involves both classification and text generation tasks. however, they addressed a completely different use case, consisting in detecting a user's emotion-aware response, rather than conversational qa. ## conclusions we proposed the multi-task learning (mtl) of passage reranking and answer extraction to share a single text generation model, so as to improve effectiveness on the open-retrieval conversational question answering (orconvqa) task. our experiments on two datasets, namely the or-quac and or-coqa datasets, showed that our proposed monoqa model has the best effectiveness on these datasets, yielding statistically significant improvements over several strong baselines from the literature, e.g. the orconvqa system proposed by @xcite and the individual application of the monot5 and unifiedqa models. our resulting system improves the best baseline by up to 12% map and 23% word-level f1 on the or-quac and or-coqa datasets. in addition, we demonstrated that including the reranker in the pipeline helps to enhance the retrieval and qa performances, yielding statistically significant improvements over the stateof-the-art two-stage pipeline baselines. furthermore, we showed that our mtl-based monoqa model improves the efficiency of inference compared to the individual application of the monot5 and unifiedqa models for separately reranking and extracting the answer to the user's question.