CL - QR : Cross-Lingual Enhanced Query Reformulation for Multi-lingual Conversational AI Agents (2023)

Abstract: The growing popularity of conversational AI agents such as Alexa, Google Assistant, and Siri rely on accurate spoken language comprehension. The query reformulation (QR) method, which reformulates defective user queries, has been broadly adopted to mitigate the challenges posed by understanding user’s intent from imperfect spoken recognition result. However, due to the scarcity of non-English QR labels, providing high-quality QR for non-English users still remains a challenge. This work proposes a novel cross-lingual QR framework, CL-QR, to leverage the abundant reformulation resources in English to improve non-English QR performance. The proposed work also proposes a Module-wise Mutually-supervised Feedback learning (MMF) algorithm to enable the continually self-improving of the CL-QR, which alleviates the lack of cross-lingual QR training data and enhances the delivery of high-quality reformulations learned in English for multilingual queries. Both offline evaluation and online A/B testing demonstrates the effectiveness of the proposed method.

## introduction conversational ai agents like alexa, siri, and google assistant are becoming ubiquitous today. however, the inaccurate interpretation of users' queries is a critical issue, in which considerable number of interpretation failures come from the frictions induced by either automatic speech recognition (asr) error, semantic ambiguity, or defective user expressions. query reformulation (qr) techniques @xcite @xcite have been widely adopted to improve the comprehension of user intentions for ai agents. for instance, for the query "play old town load" which contains an asr error, qr system is employed to fix it to the correct one "play old town road"; and for the query "where is danube" which is incomplete, qr system can reformulate it to "where is the danube river". building productionlevel qr systems requires substantial amounts of user-agent interactive data (e.g., user queries, implicit user feedback, and user rephrases) @xcite @xcite . the qr systems, however, struggle to provide high-quality reformulations for non-english queries due to the limited number of non-english users/traffic. to address this challenge, this work proposes a novel cross-lingual query reformulation (cl-qr) system that effectively leverages the abundant query reformulation resources in english to generate superior reformulations for non-english queries. figure 1 demonstrates several cross-lingual reformulation examples that can be achieved by cl-qr. the defective non-english queries are first mapped to english to get the english reformulations, and then transferred to final reformulations in the original language. cl-qr comprises three major components: cross-lingual reformulation extraction, crosslingual reformulation plausibility detection, and back translation. the cross-lingual reformulation extraction aims at extracting all potential english reformulations for a defective non-english query; the cross-lingual reformulation plausibility detection predicts the plausibility score for each crosslingual qr pair and the score is used to select the most suitable english reformulation; the back translation is used to translate the most suitable english reformulation back to the original language. to address the challenge of insufficient cl train-ing data, this paper proposes a novel module-wise mutually-supervised feedback learning (mmf) algorithm. specifically, the plausibility detection module and the back-translation module can be trained in a mutually reinforcing manner, in which each module's output can be used as a weakly feedback label to supervise the other module's training. in this way, the cl-qr can be initially trained on a small set of golden cross-lingual qr pairs, and then continually improves itself using readily available large-scale mono-lingual non-english qr pairs. the offline evaluation and online a/b test on spanish, italian, and french demonstrate the efficacy of the proposed cl-qr. query reformulation: query reformulation aims to correct the frictions in user utterances in voice controlled ai agent, and has been widely studied. for example, search/retrieval based methods are used to find the ideal reformulation from a collection of successful utterance @xcite @xcite , generation methods are applied to generate the reformulation directly @xcite , and markov chain models coverage query reformulation patterns based on the collaborative filtering mechanism @xcite . although these methods have achieved great performance on english conversational ai system, their performances on non-english languages are suboptimal because of the data sparsity challenges. cross-lingual knowledge transferring: pretrained multilingual language models like mbert @xcite , xlm-r @xcite and mbart @xcite have opened the doors for cross-lingual transfer learning, particularly focusing on transferring knowledge from resource-rich languages to resource-scarce languages. such idea has been widely studied and applied in various tasks such as question answering @xcite , keyphrase generation @xcite , e-commerce product search @xcite , named entity recognition (ner) @xcite , natural language understanding (nlu) @xcite , etc. in this work, we adopt the idea for qr task, based on the fact that many qr patterns are shareable across languages (e.g. fixing the asr error in entities, completing a user query, etc.). ## methodology this section describes cl-qr in details. given a defective query in language-x (non-english) as input (x q ), our ultimate goal is to find a proper nondefective reformulation of it (x r ). to achieve this, the cl-qr framework takes 3 steps as illustrated in figure 2a : 1) map x q to the corresponding english query en q (usually also defective) through cross-lingual retrieval or translation, and then perform qr in english to get en q 's non-defective reformulations en r ; 2) use the plausibility detection module to select the most appropriate en r for x q , from the multiple candidates from step 1); 3) finally, the language-x reformulation x r is achieved by translating the selected en r back into language-x. ## conclusion in this work, a novel cross-lingual query reformulation (cl-qr) method is proposed to address the limitations of existing qr systems for non-english queries by leveraging large-scale of qr resources in english to generate reformulations for low-resource non-english queries. the cl-qr also includes a novel plausibility detection module to select the best cross-lingual reformulations. additionally, a module-wise mutually-supervised feedback training strategy is proposed for effective self-training. the proposed cl-qr method has been rigorously evaluated through offline testing and online a/b experiments conducted for spanish, italian, and french traffic. the promising results verify the effectiveness of this framework. and data protection. for example, the extraction of cross-lingual reformulation may involve the collection and processing of large amounts of user data. it is important to ensure that the collection and processing of this data is done in a way that protects user privacy and that user data is not misused or mishandled. additionally, the use of cl-qr may raise concerns about linguistic and cultural bias in the data used to train the model. it is also important to ensure that the method is designed in a way that takes into account the diverse linguistic and cultural backgrounds of the users it serves in this work, all the data used for training / testing are from data with identification information removed. this means that users' personal information have been removed and will not be fed into the models. besides, the production pipeline also includes several guardrails to filter out inappropriate reformulations, to ensure the cross-lingual reformulation doesn't include any cultural bias.