Query suggestion (QS) in web search must provide efficient and up-to-date suggestions that are relevant to the session context. Recent studies treat QS as generation, which captures session context well but suffers from high latency and costly retraining to maintain information freshness. In this study, we recast QS as dense retrieval: given a search session, the next query is retrieved from a large index of historical queries using efficient approximate nearest neighbor search. We propose Context-aware Asymmetric Dual Encoder for QS (CADE-QS), which uses an asymmetric dual encoder to model query -to- suggestion dependency and incorporates session history into the query encoder via context-aware contrastive learning. We evaluate our method on two real-world search-log datasets: a recent Japanese web search log and the public AOL log. CADE-QS rivals strong generative baselines in quality while reducing end-to-end latency to around 30 ms on CPU, representing an order-of-magnitude improvement. Detailed analyses confirm CADE-QS's robust context awareness, unidirectional modeling, and practicality for reflecting evolving information via index refreshes, as well as the effectiveness of an adaptive hybrid strategy for low-coverage scenarios. Our code is publicly available at https://github.com/lycorp-jp/cadeqs.

Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific taskoriented spoken dialogue systems and open-domain non-task-oriented ones.To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system.To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15, 160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity).In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection.Experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F 1 -score from 86.21 to 87.53.

This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives.Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update.To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity.Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting).IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation.However, the complexity of these components hinders developers from determining which component causes an error.To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes.We evaluate the method using the user logs of a commercial IA.The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.

Intelligent assistants on mobile devices, such as Siri, have recently gained considerable attention as novel applications of dialogue technologies.A tremendous amount of real users of intelligent assistants provide us with an opportunity to explore a novel task of predicting whether users will continually use their intelligent assistants in the future.We developed prediction models of prospective user engagement by using large-scale user logs obtained from a commercial intelligent assistant.Experiments demonstrated that our models can predict prospective user engagement reasonably well, and outperforms a strong baseline that makes prediction based past utterance frequency.

We propose a method that learns a crosslingual projection of word representations from one language into another.Our method utilizes translatable context pairs as bonus terms of the objective function.In the experiments, our method outperformed existing methods in three language pairs, (English, Spanish), (Japanese, Chinese) and (English, Japanese), without using any additional supervisions.

Microblogs have recently received widespread interest from NLP researchers.However, current tools for Japanese word segmentation and POS tagging still perform poorly on microblog texts.We developed an annotated corpus and proposed a joint model for overcoming this situation.Our annotated corpus of microblog texts enables not only training of accurate statistical models but also quantitative evaluation of their performance.Our joint model with lexical normalization handles the orthographic diversity of microblog texts.We conducted an experiment to demonstrate that the corpus and model substantially contribute to boosting accuracy.

This paper describes a method of translating a predicate-argument structure of a verb into that of an equivalent verb, which is a core component of the dictionary-based paraphrasing. Our method grasps several usages of a headword and those of the def-heads as a form of their case frames and aligns those case frames, which means the acquisition of word sense disambiguation rules and the detection of the appropriate equivalent and case marker transformation.