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{
"paper_id": "2022",
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"date_generated": "2023-01-19T07:12:51.580455Z"
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"title": "Can Question Rewriting Help Conversational Question Answering?",
"authors": [
{
"first": "Etsuko",
"middle": [],
"last": "Ishii",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The Hong Kong University of Science and Technology",
"location": {}
},
"email": "eishii@connect.ust.hk"
},
{
"first": "Yan",
"middle": [],
"last": "Xu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The Hong Kong University of Science and Technology",
"location": {}
},
"email": ""
},
{
"first": "Samuel",
"middle": [],
"last": "Cahyawijaya",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The Hong Kong University of Science and Technology",
"location": {}
},
"email": "scahyawijaya@connect.ust.hk"
},
{
"first": "Bryan",
"middle": [],
"last": "Wilie",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The Hong Kong University of Science and Technology",
"location": {}
},
"email": "bwilie@connect.ust.hk"
}
],
"year": "",
"venue": null,
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"abstract": "Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA. We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.",
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"abstract": [
{
"text": "Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA. We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.",
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"section": "Abstract",
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"text": "The question rewriting (QR) task has been introduced as a mitigation method for conversational question answering (CQA). CQA asks a machine to answer a question based on the provided passage and a multi-turn dialogue (Reddy et al., 2019; Choi et al., 2018) , which poses an additional challenge to comprehend the dialogue history. To ease the challenge, QR aims to teach a model to paraphrase a question into a self-contained format using its dialogue history (Elgohary et al., 2019a; Anantha et al., 2021a) . Except for Kim et al. (2021) , however, no one has provided evidence that QR is effective for CQA in practice. Existing works on QR often (i) depend on the existence of a QR dataset for every target CQA dataset, and (ii) focus more on generating high-quality rewrites than improving CQA performance, making them unsatisfactory for the justification of QR.",
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"start": 217,
"end": 237,
"text": "(Reddy et al., 2019;",
"ref_id": "BIBREF19"
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{
"start": 238,
"end": 256,
"text": "Choi et al., 2018)",
"ref_id": "BIBREF4"
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"start": 460,
"end": 484,
"text": "(Elgohary et al., 2019a;",
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"start": 485,
"end": 507,
"text": "Anantha et al., 2021a)",
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"text": "Kim et al. (2021)",
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"section": "Introduction",
"sec_num": "1"
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"text": "To verify the effectiveness of QR, we explore a reinforcement learning (RL) approach that integrates QR and CQA tasks without corresponding labeled QR datasets. In the RL framework, a QR model plays the role of \"the agent\" that receives * Equal Contribution Figure 1 : Overview of the RL approach. The current question Q t and its dialogue history are reformulated into a self-contained questionQ t by the QR model. Then,Q t is passed to the QA model to extract an answer span\u00c3 t from the evidence document. We train the QR model by maximizing the reward obtained by comparing the predicted answer span\u00c3 t with the gold span A t .",
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"text": "Figure 1",
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"section": "Introduction",
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"text": "rewards from a QA model that acts as \"the environment.\" During training, the QR model aims to maximize the performance on the CQA task by generating better rewrites of the questions.",
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"section": "Introduction",
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"text": "Despite the potential and plausibility of the RL approach, our experimental results suggest an upper bound of the performance, and it is on par with the baselines without QR. In this paper, we provide analysis to (i) understand the reason for the failure of the RL approach and (ii) reveal that QR cannot improve CQA performance even with the non-RL approaches. The code is available at https: //github.com/HLTCHKUST/cqr4cqa.",
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"section": "Introduction",
"sec_num": "1"
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"text": "The CQA task aims to assist users in seeking information (Reddy et al., 2019; Choi et al., 2018; Campos et al., 2020 solve the conversation history and understand a highly-contextualized question. Most prior works focus on model structures (Zhu et al., 2018; Yeh and Chen, 2019; Zhang et al., 2021b; Zhao et al., 2021) or training techniques (Ju et al., 2019; Xu et al., 2021) to improve the performance. QR tasks have been proposed to further improve CQA systems by paraphrase a question into a self-contained styles (Elgohary et al., 2019a; Petr\u00e9n Bach Hansen and S\u00f8gaard, 2020; Anantha et al., 2021a) . While many of the existing works on QR put more effort toward generating high-quality rewrites (Lin et al., 2020; Vakulenko et al., 2021) , Kim et al. (2021) introduced a framework to leverage QR to finetune CQA models with a consistency-based regularization. QR has also been studied in single-turn QA and other information-seeking tasks (Nogueira and Cho, 2017; Buck et al., 2018) .",
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"text": "(Reddy et al., 2019;",
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"text": "Choi et al., 2018;",
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"text": "Campos et al., 2020",
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"text": "(Zhu et al., 2018;",
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"text": "Yeh and Chen, 2019;",
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"text": "Zhang et al., 2021b;",
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"start": 300,
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"text": "Zhao et al., 2021)",
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"start": 342,
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"text": "(Ju et al., 2019;",
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{
"start": 360,
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"text": "Xu et al., 2021)",
"ref_id": "BIBREF22"
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"text": "(Elgohary et al., 2019a;",
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"text": "Petr\u00e9n Bach Hansen and S\u00f8gaard, 2020;",
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"text": "Anantha et al., 2021a)",
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"text": "(Lin et al., 2020;",
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"text": "Vakulenko et al., 2021)",
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"text": "Kim et al. (2021)",
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"text": "(Nogueira and Cho, 2017;",
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"section": "Related Work",
"sec_num": "2"
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"text": "We denote a CQA dataset as {D n } N n=1 and the dialogue history at turn t as",
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"section": "Methodology",
"sec_num": "3"
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"text": "D t = {(Q i , A i )} t i=1",
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"section": "Methodology",
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"text": ", where Q t is the question and A t is the answer. Along with the QA pairs, the corresponding evidence documents Y t are also given.",
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"section": "Methodology",
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"text": "As depicted in Figure 1 , our proposed RL framework involves a QA model as an environment and a QR model as an agent. LetQ t = {q l } L l=1 denote a generated rewritten question sequence of Q t . The objective of the QR model is to rewrite the question Q t at turn t into a self-contained version, based on the current question and the dialogue history D t\u22121 . The agent takes an input state X t = (D t\u22121 , Q t ) and generates a paraphraseQ t . Then,X t = (D t\u22121 ,Q t ) and an evidence document Y t are provided to an environment, namely, the QA model f \u03d5 , which extracts an answer spa\u00f1",
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"start": 15,
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"text": "Figure 1",
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"text": "A t = f \u03d5 (X t , Y t ).",
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"section": "Methodology",
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"text": "We aim for the agent, a QR model \u03c0 \u03b8 , to learn to generate a high-quality para-phrase of the given question based on the reward received from the environment.",
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"section": "Methodology",
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"text": "The policy, in our case the QR model, assigns probability",
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"text": "\u03c0 \u03b8 (Q t |X t ) = L l=1 p(q l |q 1 , . . . ,q l\u22121 , X t ). (1)",
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"text": "Our goal is to maximize the expected reward of the answer returned under the policy, namely,",
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},
{
"text": "EQUATION",
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{
"start": 0,
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"text": "EQUATION",
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"text": "where r is a reward function. We apply the tokenlevel F1-score between the predicted answer spa\u00f1 A t and the gold span A t as the reward r. We can directly optimize the expected reward in Eq. 2 using RL algorithms. Prior to the training process, the QA model f \u03d5 is fine-tuned on {D n } and the QR model is initialized with \u03c0 \u03b8 = \u03c0 \u03b8 0 , where \u03c0 \u03b8 0 is a pretrained language model. We apply Proximal Policy Optimization (PPO) (Schulman et al., 2017; Ziegler et al., 2019) to train \u03c0 \u03b8 . PPO is a policy gradient method which alternates between sampling data through interaction with the environment and optimizing a surrogate objective function via stochastic gradient ascent. Following Ziegler et al. 2019, we apply a KL-penalty to the reward r so as to prevent the policy \u03c0 \u03b8 from drifting too far away from \u03c0 \u03b8 0 :",
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"text": "R t = R(X t ) = r(f \u03d5 (X t )) \u2212 \u03b2KL(\u03c0 \u03b8 , \u03c0 \u03b8 0 ),",
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"text": "where \u03b2 represents a weight factor and R t is the modified reward of r.",
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"text": "We use a pretrained RoBERTa (Liu et al., 2019) model as the initial QA model and adapt it to the CQA tasks. For the QR models, we leverage pretrained GPT-2 and first finetune them with QR datasets for better initialization. We attempt three settings: (a) directly fine-tune the QA model on the CQA datasets (end-to-end), (b) fine-tune the QA model with questions rewritten by the QR model (pipeline), and (c) train the QR model based on the reward obtained from the QA model. More details of the experiments can be found in Appendix A.",
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"text": "Datasets We conduct our experiments on two crowd-sourced CQA datasets, CoQA (Reddy et al., 2019) and QuAC (Choi et al., 2018) . Since the test set is not publicly available for both CoQA and QuAC, following Kim et al. 2021, we randomly sample 5% of dialogues in the training set and adopt them as our validation set and report the test results on the original development set for the CoQA experiments. We apply the same split as Kim et al. (2021) for the QuAC experiments. For the QR model pre-training, we use two QR datasets: QReCC (Anantha et al., 2021b) and CA-NARD (Elgohary et al., 2019b) . CANARD is generated by rewriting a subset of the original questions in the QuAC datasets, and contains 40K questions in total. QReCC is built upon three publicly available datasets: QuAC, TREC Conversational Assistant Track (CAsT) (Dalton et al., 2020) and Natural Questions (NQ) (Kwiatkowski et al., 2019) . QReCC contains 14K dialogues with 80K questions, and 9.3K dialogues are from QuAC.",
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"text": "Evaluation Metrics Following the leaderboards, we utilize the unigram F1 score to evaluate the QA performance. In CoQA evaluation, the QA models are also evaluated with the domain-wise F1 score. In QuAC evaluation, we incorporate the human equivalence score HEQ-Q and HEQ-D as well. HEQ-Q indicates the percentage of questions on which the model outperforms human beings and HEQ-D represents the percentage of dialogues on which the model outperforms human beings for all questions in the dialogue.",
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"text": "We report our experimental results in Table 1 . We see that our RL approach yields 0.9-1.6 F1 improvement over the pipeline setting regardless of the dataset combinations and performs almost as well as the end-to-end setting. This partially supports our expectation that RL lifts the CQA performance. However, we find it almost impossible to bring significant improvement over the end-to-end baseline despite our extensive trials. One reason why we cannot provide as much improvement as reported in Kim et al. (2021) would be related to the inputs of the QA model. Their EXCORD feeds the original questions together with the rewritten questions, whereas we only use the rewritten questions. It is also noteworthy that their results are consistently lower than ours, even lower than our end-to-end settings.",
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"text": "Our inspection of the questions generated by the QR models reveals that the models learn to copy the original questions by PPO training, and this is the direct reason that our method cannot outperform the end-to-end baselines. Indeed, on average, 89.6% of the questions are the same as the original questions after PPO training, although this value is 34.5% in the pipeline settings. We also discover a significant correlation between the performance and how much the QR models copy the original question (the correlation coefficient is 0.984 for CoQA and 0.967 for QuAC) and the edit distance from the original question (the correlation coefficient is -0.996 for CoQA and -0.989 for QuAC).",
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"text": "In this section, we provide an analysis to (i) raise a sensitivity problem of the QA model to explain the failure of RL and (ii) disclose that there is no justification for QR, even in the non-RL approaches.",
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"text": "It appears that the QA models are more sensitive to trivial changes than the reward models in other successful language generation tasks, and this could -) 96.7 (-1.5) 74.8 (-9.8) 57.4 (-10.5) SLW 91.9 (-3.9) 97.0 (-1.1) 83.0 (-1.6) 66.7 (-1.1) WIF 94.3 (-1.5) 97.7 (-0.5) 82.6 (-2.0) 65.6 (-2.2) SPP 94.8 (-1.0) 97.7 (-0.5) 78.3 (-6.2) 65.5 (-2.4) Table 4 : Results of the supervised learning approach. \"XX Model\" denotes the QA model trained on XX, and EM the percentage of the predictions the same as the gold.",
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"text": "account for our lower performance on CQA. As can be seen from the examples in Table 2 , a subtle alteration such as uppercasing or replacement with synonyms can significantly change F1 scores.",
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"text": "To quantify the sensitivity of the reward models, we compare model robustness between our QA models and sentiment analysis models that have been reported in Ziegler et al. (2019) to be effective for stylistic language generation. We adopt publicly available models that are fine-tuned sentiment analysis datasets: BERT-based trained on Amazon polarity (McAuley and Leskovec, 2013) 1 and RoBERTa-base trained on Yelp polarity (Zhang et al., 2015) 2 . To test the robustness of the models, we introduce small perturbations to the samples in the test set using the NL-Augmenter toolkit (Dhole et al., 2021) , and compare F1 scores on each task (experimental details in Appendix B).",
"cite_spans": [
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"start": 425,
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"text": "Based on the robustness test given in Table 3 , the QA models are shown to be significantly less robust against most perturbations compared to the sentiment analysis models. It is conceivable that this sensitivity of the QA model leads to a sparse reward problem for the agent, which causes instability for the model learning the optimal policy. An important direction for future studies is to ease the sparse reward problem by, for example, enhancing the robustness of the QA models.",
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"text": "1 https://huggingface.co/fabriceyhc/ bert-base-uncased-amazon_polarity 2 https://huggingface.co/VictorSanh/ roberta-base-finetuned-yelp-polarity",
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"sec_num": "5.1"
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"text": "CoQA QuAC Table 5 : Results of the data augmentation approach. EM denotes the percentage of the predictions the same as the gold.",
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"text": "First, we evaluate with a simple supervised learning approach using rewrites provided by CANARD. Extracting the QuAC samples that have a CA-NARD annotation, we (i) evaluate the CANARD annotations with the QA model trained on QuAC (the model used in the main experiments) and (ii) train another QA model with the CANARD annotations. Training is under the same conditions of the QA model initialization as in the main experiments. As the results in Table 4 show, we can hardly observe the effectiveness of the CANARD annotations. This supports the claim in Buck et al. (2018) that better rewrites in the human eye are not necessarily better for machines and implies the difficulty of exploiting QR for CQA.",
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"start": 555,
"end": 573,
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"text": "Moreover, we explore a data-augmentation approach to integrate QR and CQA. First, we generate ten possible rewrites using top-k sampling (Zhang et al., 2021a) for all the questions of the CQA datasets. To guarantee the quality of the rewrites, we select the best F1 scoring ones from every ten candidates and use them to teach another QR model how to reformulate questions (experimental details in Appendix C). As the results in Table 5 show, we consistently get worse scores compared to the end-to-end settings in CoQA, and almost the same scores for QuAC, not finding justification to apply QR in the manner of the data augmentation approach.",
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"start": 137,
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"text": "In this paper, we explore the RL approach to verify the effectiveness of QR in CQA, and report that the RL approach is on par with simple end-to-end baselines. We find the sensitivity of the QA models would disadvantage the RL training. Future work is needed to verify that QR is a promising mitigation method for CQA since even the non-RL approaches perform unsatisfactorily.",
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"TABREF1": {
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"content": "<table><tr><td>Models</td><td/><td>Overall F1</td><td>Child.</td><td>CoQA Liter.</td><td>M&H</td><td>News</td><td>Wiki.</td><td>F1</td><td>QuAC HEQ-Q</td><td>HEQ-D</td></tr><tr><td/><td>end-to-end</td><td>84.5</td><td>84.4</td><td>82.4</td><td>82.9</td><td>86.0</td><td>86.9</td><td>67.8</td><td>63.5</td><td>7.9</td></tr><tr><td>QReCC</td><td>pipeline ours</td><td>82.9 84.7</td><td>82.9 84.3</td><td>80.9 83.1</td><td>81.5 82.7</td><td>84.4 86.3</td><td>84.8 86.8</td><td>66.3 67.6</td><td>62.0 63.2</td><td>6.6 7.8</td></tr><tr><td/><td>pipeline</td><td>82.8</td><td>83.4</td><td>80.1</td><td>80.8</td><td>84.4</td><td>85.6</td><td>66.5</td><td>62.5</td><td>7.4</td></tr><tr><td>CANARD</td><td colspan=\"10\">EXCORD \u2020 83.4 (+0.6) 84.4 (1.9) 81.2 (+1.0) 79.8 (-0.3) 84.6 (+0.3) 87 (0.0) 67.7 (+1.2) 64.0 (+1.6) 9.3 (+2.1) ours 84.4 84.1 82.7 82.6 86.0 86.7 67.4 62.7 8.1</td></tr></table>",
"text": "). The key challenge is to re-"
},
"TABREF2": {
"num": null,
"html": null,
"type_str": "table",
"content": "<table/>",
"text": "Evaluation results of our approach and baselines on the test set. EXCORD \u2020 follows the results reported byKim et al. (2021) and (\u00b1x.x) indicate the improvement compared to their original baseline. Bold are the best results amongst all. Underlined represents the best score on each combination of the CQA and QR datasets."
},
"TABREF3": {
"num": null,
"html": null,
"type_str": "table",
"content": "<table><tr><td>Question</td><td>F1 Score</td><td>Question</td><td>F1 Score</td></tr><tr><td>Q t</td><td/><td/><td/></tr></table>",
"text": "What is the Vat the library of? 1.0 Q t Where did the band The Smashing Pumpkins put on display? 1.0 Q t What is the Vat the Library of? 0.22Q t Where was the band The Smashing Pumpkins put on display? 0.0 Q t What was everybody doing? 0.91 Q t Which company produced the movie Island of Misfit Toys? 1.0 Q t What was everyone doing? 0.0Q t Which company produced the movie, The Island of Misfit Toys? 0.0"
},
"TABREF4": {
"num": null,
"html": null,
"type_str": "table",
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"text": "Minor modification of questions may cause a drastic change in CQA performance."
},
"TABREF6": {
"num": null,
"html": null,
"type_str": "table",
"content": "<table><tr><td>Datasets</td><td colspan=\"4\">QuAC Model CANARD Model F1 EM F1 EM</td></tr><tr><td>QuAC</td><td>67.7</td><td>51.5</td><td>62.9</td><td>46.8</td></tr><tr><td colspan=\"2\">CANARD 65.1</td><td>49.9</td><td>63.3</td><td>46.9</td></tr></table>",
"text": "Robustness test on Sentiment Analysis and CQA tasks. We apply four perturbations: UPC (upper casing), SLW (slang word), WIF (word inflection), and SPP (sentence paraphrasing)."
},
"TABREF7": {
"num": null,
"html": null,
"type_str": "table",
"content": "<table><tr><td>F1</td><td>EM</td><td>F1</td><td>EM</td></tr><tr><td>end-to</td><td/><td/><td/></tr></table>",
"text": "-end 84.5 76.4 67.83 51.47 QReCC 84.1 76.0 67.83 51.48 CANARD 83.7 75.8 67.81 51.50"
}
}
}
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