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Register a new userPersonalized Search-based Query Rewrite System for Conversational AI
نظام إعادة كتابة الاستعلام المستندة إلى البحث في البحث
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Requery Rewrite (QR) هو مكون ناشئ في أنظمة المحادثة AI، مما يقلل من عيب المستخدم.سبب عيب المستخدم لأسباب مختلفة، مثل الأخطاء في نظام الحوار المنطوق أو عروض المستخدمين للسان أو لغتهم المختصرة.ينبع العديد من عيوب المستخدمين من العوامل الشخصية، مثل نمط خطاب المستخدم أو اللهجة أو التفضيلات.في هذا العمل، نقترح إطار عمل QR القائم على البحث شخصي، والذي يركز على التخفيض التلقائي لعيب المستخدم.نقوم ببناء مؤشر شخصي لكل مستخدم، يشمل طبقات تقارب متنوعة لتعكس التفضيلات الشخصية لكل مستخدم في منظمة العفو الدولية المحادثة.يحتوي نظام QR الشخصي الخاص بنا على طبقات استرجاع وترتيب.بدعم من التعلم القائم على ملاحظات المستخدم، تدريب نماذجنا لا يتطلب بيانات مشروح يدوية.أظهرت التجارب على مجموعة الاختبارات الشخصية أن نظام QR الشخصي الخاص بنا قادر على تصحيح أخطاء النظامية والمستخدم باستخدام المدخلات الصوتية والدلية.
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users' slips of the tongue or their abridged language. Many of the user defects stem from personalized factors, such as user's speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.
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Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search. Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model. The state-of-the-art Transformer-bas
ed models can effectively learn textual semantics from user session logs, but they often ignore users' geographic location information that is crucial for the Point-of-Interest (POI) search of map services. In this paper, we proposed a pre-training model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs. Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities. Then we use graph representation learning methods to get geographic representations. Finally, we train a BERT-like pre-training model with text and POIs' graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search. The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.
The next generation of conversational AI systems need to: (1) process language incrementally, token-by-token to be more responsive and enable handling of conversational phenomena such as pauses, restarts and self-corrections; (2) reason incrementally
allowing meaning to be established beyond what is said; (3) be transparent and controllable, allowing designers as well as the system itself to easily establish reasons for particular behaviour and tailor to particular user groups, or domains. In this short paper we present ongoing preliminary work combining Dynamic Syntax (DS) - an incremental, semantic grammar framework - with the Resource Description Framework (RDF). This paves the way for the creation of incremental semantic parsers that progressively output semantic RDF graphs as an utterance unfolds in real-time. We also outline how the parser can be integrated with an incremental reasoning engine through RDF. We argue that this DS-RDF hybrid satisfies the desiderata listed above, yielding semantic infrastructure that can be used to build responsive, real-time, interpretable Conversational AI that can be rapidly customised for specific user groups such as people with dementia.
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowle
dge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.
Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrieval-based chatbots is still in the early stage. Existing works in ret
rieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-and-rewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-the-shelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.
This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations. Prior to our work, the state-of-the-art approach uses a multi-stage pipeline comprising conversation
al query reformulation and information retrieval modules. Despite its effectiveness, such a pipeline often includes multiple neural models that require long inference times. In addition, independently optimizing each module ignores dependencies among them. To address these shortcomings, we propose to integrate conversational query reformulation directly into a dense retrieval model. To aid in this goal, we create a dataset with pseudo-relevance labels for conversational search to overcome the lack of training data and to explore different training strategies. We demonstrate that our model effectively rewrites conversational queries as dense representations in conversational search and open-domain question answering datasets. Finally, after observing that our model learns to adjust the L2 norm of query token embeddings, we leverage this property for hybrid retrieval and to support error analysis.
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