Signal Temporal Logic (STL) is a powerful formal language for specifying real-time specifications of Cyber-Physical Systems (CPS). Transforming specifications written in natural language into STL formulas automatically has attracted increasing attention. Existing rule-based methods depend heavily on rigid pattern matching and domain-specific knowledge, limiting their generalizability and scalability. Recently, Supervised Fine-Tuning (SFT) of large language models (LLMs) has been successfully applied to transform natural language into STL. However, the lack of fine-grained supervision on atomic proposition correctness, semantic fidelity, and formula readability often leads SFT-based methods to produce formulas misaligned with the intended meaning. To address these issues, we propose RESTL, a reinforcement learning (RL)-based framework for the transformation from natural language to STL. RESTL introduces multiple independently trained reward models that provide fine-grained, multi-faceted feedback from four perspectives, i.e., atomic proposition consistency, semantic alignment, formula succinctness, and symbol matching. These reward models are trained with a curriculum learning strategy to improve their feedback accuracy, and their outputs are aggregated into a unified signal that guides the optimization of the STL generator via Proximal Policy Optimization (PPO). Experimental results demonstrate that RESTL significantly outperforms state-of-the-art methods in both automatic metrics and human evaluations.

As Large Language Models (LLMs) become more advanced, the security risks they pose also increase. Ensuring that LLM behavior aligns with human values, particularly in mitigating jailbreak attacks with elusive and implicit intentions, has become a significant challenge. To address this issue, we propose a jailbreak defense method called Real Intentions Defense (RID), which involves two phases: soft extraction and hard deletion. In the soft extraction phase, LLMs are leveraged to extract unbiased, genuine intentions, while in the hard deletion phase, a greedy gradient-based algorithm is used to remove the least important parts of a sentence, based on the insight that words with smaller gradients have less impact on its meaning. We conduct extensive experiments on Vicuna and Llama2 models using eight state-of-the-art jailbreak attacks and six benchmark datasets. Our results show a significant reduction in both Attack Success Rate (ASR) and Harmful Score of jailbreak attacks, while maintaining overall model performance. Further analysis sheds light on the underlying mechanisms of our approach.

Conversational Recommendation Systems (CRS) effectively address information asymmetry by dynamically eliciting user preferences through multi-turn interactions. However, existing CRS methods commonly assume that users have clear, definite preferences for one or multiple target items. This assumption can lead to over-trusting user feedback, treating accepts/rejects as definitive signals to filter items and reduce the candidate space, potentially causing over-filtering and excluding relevant alternatives. In reality, users often exhibit vague preferences, lacking well-defined inclinations for certain attribute types (e.g., color, pattern), and their decision-making process during interactions is rarely binary. Instead, users’ choices are relative, reflecting a range of preferences rather than strict likes or dislikes. To address this issue, we introduce a novel scenario called Vague Preference Multi-Round Conversational Recommendation (VPMCR), which employs a soft estimation mechanism to assign non-zero confidence scores to all candidate items, accommodating users’ vague and dynamic preferences while mitigating over-filtering. In the VPMCR setting, we introduce a solution called Vague Preference Policy Learning (VPPL), which consists of two main components: Ambiguity-Aware Soft Estimation (ASE) and Dynamism-Aware Policy Learning (DPL). ASE aims to accommodate the ambiguity in user preferences by estimating preference scores for both directed and inferred preferences, employing a choice-based approach and a time-aware preference decay strategy. DPL implements a policy learning framework, leveraging the preference distribution from ASE, to guide the conversation and adapt to changes in users’ preferences for making recommendations or querying attributes. Extensive experiments conducted on diverse datasets demonstrate the effectiveness of VPPL within the VPMCR framework, outperforming existing methods and setting a new benchmark for CRS research. Our work represents a significant advancement in accommodating the inherent ambiguity and relative decision-making processes exhibited by users, improving the overall performance and applicability of CRS in real-world settings.

Multi-agent collaboration is the latest trending method to build conversational recommender systems (CRS), especially with the widespread use of Large Language Models (LLMs) recently. Typically, these systems employ several LLM agents, each serving distinct roles to meet user needs. In an industrial setting, it’s essential for a CRS to exhibit low first token latency (i.e., the time taken from a user’s input until the system outputs its first response token.) and high scalability—for instance, minimizing the number of LLM inferences per user request—to enhance user experience and boost platform profit. For example, JD.com’s baseline CRS features two LLM agents and a search API but suffers from high first token latency and requires two LLM inferences per request (LIPR), hindering its performance. To address these issues, we introduce a Hybrid Multi-Agent Collaborative Recommender System (Hybrid-MACRS). It includes a central agent powered by a fine-tuned proprietary LLM and a search agent combining a related search module with a search engine. This hybrid system notably reduces first token latency by about 70% and cuts the LIPR from 2 to 1. We conducted thorough online A/B testing to confirm this approach’s efficiency.

In the multi-turn dialogue reasoning task, existing models conduct word-level interaction on the entire context to gather reasoning evidence, which aims to select the logically correct one from the candidate response options. Observing the fact that the salient reasoning evidence usually comes from certain snippets of the whole dialogue session, one promising study direction is to explicitly identify the candidate reasoning contexts correlated with the dialogue reasoning options, called option-related contexts, and then make logical inference among them. However, such option-related contexts are stained with noisy information. As a result, existing models may reason unfairly with biased context and select wrong options. To tackle the context bias problem, in this article, we propose a novel CounterFactual learning framework for Dialogue Reasoning, named CF-DialReas, which mitigates the bias information by subtracting the counterfactual representation from the total causal representation. Specifically, we consider two scenarios, i.e., factual dialogue reasoning where the whole context is available to estimate the total causal representation, and the counterfactual dialogue reasoning, which firstly utilizes three different types of utterance selectors to select option-unrelatedcontext, and then only the option-unrelatedcontext is available to guess the counterfactual representation. Experimental results on two public dialogue reasoning datasets show that the model with our mechanism can obtain higher ranking measures, validating the effectiveness of counterfactual learning of CF-DialReas. Further analysis on the generality of CF-DialReas shows that our counterfactual learning mechanism is generally effective to the widely-used models.

The wind shear in low altitude can bring about time-varying and unknown disturbances, which makes small fixed-wing Unmanned Aerial Vehicle (UAV) hard to accurately follow desired curved paths. In this paper, a Vector Field (VF) based curved path following algorithm is designed for UAV to overcome the above difficulties. Firstly, the path following problem is mathematically formulated to give the kinematics model of UAV with constant airspeed. Secondly, the curved path following control laws under both constant and time-varying unknown wind disturbances are designed with VF. A ground velocity estimator is additionally designed to overcome the difficulties of unmeasurable ground velocity, and the Lyapunov stability of curved path following is analyzed. Finally, both simulations and field tests on a real small fixed-wing UAV are carried out to evaluate the algorithm performance in the presence of time-varying and unknown wind disturbances. Verification results demonstrate that the algorithm proposed in this paper outperforms other widely used path following algorithms and can effectively follow arbitrary curved paths.

Weiwei Sun, Hengyi Cai, Hongshen Chen, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Designing an effective learning method remains a challenge in neural dialogue generation systems as it requires the training objective to well approximate the intrinsic human-preferred dialogue properties. Conventional training approaches such as maximum likelihood estimation focus on modeling general syntactic patterns and may fail to capture intricate conversational characteristics. Contrastive dialogue learning offers an effective training schema by explicitly training a neural dialogue model on multiple positive and negative conversational pairs. However, constructing contrastive learning pairs is non-trivial, and multiple dialogue attributes have been found to be crucial for governing the human judgments of conversations. This paper proposes to guide the response generation with attribute-aware contrastive learning to improve the overall quality of the generated responses, where contrastive learning samples are generated according to various important dialogue attributes each specializing in a different principle of conversation. Extensive experiments show that our proposed techniques are crucial to achieving superior model performance.

Yue Fang, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Bo Long, Yanyan Lan, Yanquan Zhou. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically struggle with drastically aggravated student homogenization when the number of students rises. In this paper, we propose Collaborative Group Learning, an efficient framework that aims to diversify the feature representation and conduct an effective regularization. Intuitively, similar to the human group study mechanism, we induce students to learn and exchange different parts of course knowledge as collaborative groups. First, each student is established by randomly routing on a modular neural network, which facilitates flexible knowledge communication between students due to random levels of representation sharing and branching. Second, to resist the student homogenization, students first compose diverse feature sets by exploiting the inductive bias from sub-sets of training data, and then aggregate and distill different complementary knowledge by imitating a random sub-group of students at each time step. Overall, the above mechanisms are beneficial for maximizing the student population to further improve the model generalization without sacrificing computational efficiency. Empirical evaluations on both image and text tasks indicate that our method significantly outperforms various state-of-the-art collaborative approaches whilst enhancing computational efficiency.

In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks. High-quality customer reviews can be considered as an ideal source to mine user-cared aspects. However, in reality, a large number of new products (known as long-tailed commodities) cannot gather sufficient amount of customer reviews, which brings a big challenge in the product description generation task. Existing works tend to generate the product description solely based on item information, i.e., product attributes or title words, which leads to tedious contents and cannot attract customers effectively. To tackle this problem, we propose an adaptive posterior network based on Transformer architecture that can utilize user-cared information from customer reviews. Specifically, we first extend the self-attentive Transformer encoder to encode product titles and attributes. Then, we apply an adaptive posterior distillation module to utilize useful review information, which integrates user-cared aspects to the generation process. Finally, we apply a Transformer-based decoding phase with copy mechanism to automatically generate the product description. Besides, we also collect a large-scare Chinese product description dataset to support our work and further research in this field. Experimental results show that our model is superior to traditional generative models in both automatic indicators and human evaluation.

Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better responses, some approaches have been proposed, such as feeding extra information by collecting large-scale datasets with human annotations, designing neural conversational models (NCMs) with complex architecture and loss functions, or filtering out untrustworthy samples based on a dialogue attribute, e.g., Relatedness or Genericness. In this paper, we follow the third research branch and present a data filtering method for open-domain dialogues, which identifies untrustworthy samples from training data with a quality measure that linearly combines seven dialogue attributes. The attribute weights are obtained via Bayesian Optimization (BayesOpt) that aims to optimize an objective function for dialogue generation iteratively on the validation set. Then we score training samples with the quality measure, sort them in descending order, and filter out those at the bottom. Furthermore, to accelerate the "filter-train-evaluate'' iterations involved in BayesOpt on large-scale datasets, we propose a training framework that integrates maximum likelihood estimation (MLE) and negative training method (NEG). The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively. Specifically, MLE is applied to maximize the log-likelihood of newly maintained samples, while NEG is used to minimize the log-likelihood of newly removed ones. Experimental results on two datasets show that our method can effectively identify untrustworthy samples, and NCMs trained on the filtered datasets achieve better performance.

Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.Therefore, due to the much bigger gap between various ground-truth responses and the generated synthetic response, exposure bias is more challenging in dialogue generation task.What's more, as MLE encourages the model to only learn the common words among different ground-truth responses, but ignores the interesting and specific parts, exposure bias may further lead to the common response generation problem, such as "I don't know" and "HaHa?"In this paper, we propose a novel adaptive switching mechanism, which learns to automatically transit between ground-truth learning and generated learning regarding the word-level matching score, such as the cosine similarity.Experimental results on both Chinese STC dataset and English Reddit dataset, show that our adaptive method achieves a significant improvement in terms of metric-based evaluation and human evaluation, as compared with the state-of-the-art exposure bias approaches.Further analysis on NMT task also shows that our model can achieve a significant improvement.

Knowledge-grounded dialogue generation has achieved promising performance with the engagement of external knowledge sources.Typical approaches towards this task usually perform relatively independent two sub-tasks, i.e., knowledge selection and knowledge-aware response generation.In this paper, in order to improve the diversity of both knowledge selection and knowledge-aware response generation, we propose a collaborative latent variable (CoLV) model to integrate these two aspects simultaneously in separate yet collaborative latent spaces, so as to capture the inherent correlation between knowledge selection and response generation.During generation, our proposed model firstly draws knowledge candidate from the latent space conditioned on the dialogue context, and then samples a response from another collaborative latent space conditioned on both the context and the selected knowledge.Experimental results on two widely-used knowledge-grounded dialogue datasets show that our model outperforms previous methods on both knowledge selection and response generation.

Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the sequential recommender is prone to make inconsistent predictions. In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation. Precisely, we extract the consistency knowledge by utilizing three self-supervised pre-training tasks, where temporal consistency and persona consistency capture user-interaction dynamics in terms of the chronological order and persona sensitivities, respectively. Furthermore, to provide the model with a global perspective, global session consistency is introduced by maximizing the mutual information among global and local interaction sequences. Finally, to comprehensively take advantage of all three independent aspects of consistency-enhanced knowledge, we establish an integrated imitation learning framework. The consistency knowledge is effectively internalized and transferred to the student model by imitating the conventional prediction logit as well as the consistency-enhanced item representations. In addition, the flexible self-supervised imitation framework can also benefit other student recommenders. Experiments on four real-world datasets show that SSI effectively outperforms the state-of-the-art sequential recommendation methods.

Haolan Zhan, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Yongjun Bao, Yanyan Lan. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.

Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. To facilitate the modeling of these triples, we further propose a novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives. Experimental results demonstrate that our method effectively boosts the informativeness and coherence of the generated response on both automatic and human evaluation, which verifies the advantages of considering two assessment perspectives.

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm.As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model.However, due to the open-ended nature of human conversations, the quality of user-generated training data varies greatly, and effective training samples are typically insufficient while noisy samples frequently appear.This impedes the learning of those data-driven neural dialogue models.Therefore, effective dialogue learning requires not only more reliable learning samples, but also fewer noisy samples.In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.In particular, the data manipulation model selectively augments the training samples and assigns an importance weight to each instance to reform the training data.Note that, the proposed data manipulation framework is fully data-driven and learnable.It not only manipulates training samples to optimize the dialogue generation model, but also learns to increase its manipulation skills through gradient descent with validation samples.Extensive experiments show that our framework can improve the dialogue generation performance with respect to various automatic evaluation metrics and human judgments.

A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users' expressed emotion.The task of empathetic dialogue generation is proposed to address this problem.The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work.In response to this problem, we propose a multi-resolution adversarial model -EmpDG, to generate more empathetic responses.EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion.In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues.Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge.More importantly, the conversation scenarios are further internalized using imitation learning framework, where the conventional dialogue model that has no access to future conversations is effectively regularized by transferring the scenario knowledge contained in hierarchical supervising signals from the scenario-based dialogue model, so that the future conversation is not required in actual inference.Extensive evaluations show that our approach significantly outperforms state-of-theart baselines on diversity and relevance, and expresses scenario-specific knowledge.

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly. However, existing models usually use word or sentence level similarities to detect the relevant contexts, which fail to well capture the topical level relevance. In this paper, we propose a new model, named STAR-BTM, to tackle this problem. Firstly, the Biterm Topic Model is pre-trained on the whole training dataset. Then, the topic level attention weights are computed based on the topic representation of each context. Finally, the attention weights and the topic distribution are utilized in the decoding process to generate the corresponding responses. Experimental results on both Chinese customer services data and English Ubuntu dialogue data show that STAR-BTM significantly outperforms several state-of-the-art methods, in terms of both metric-based and human evaluations.

Nowadays, intelligent information systems, especially the interactive information systems (e.g., conversational interaction systems like Siri, and Cortana; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with users explicitly through natural languages, or mine users interests and respond to users requests implicitly. Interactivity has become a crucial element towards intelligent information systems. Despite the fact that interactive information systems have gained significant progress, there are still many challenges to be addressed when applying these models to real-world scenarios. This half day workshop explores challenges and potential research, development, and application directions in applied interactive information systems. We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks. We welcome practical, theoretical, experimental, and methodological studies that advances the interactivity towards intelligent information systems. The workshop aims to bring together a diverse set of practitioners and researchers interested in investigating the interaction between human and information systems to develop more intelligent information systems.

Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user experience. A simple and effective way is to extract keywords directly from the knowledge-base of products, i.e., attributes or title, as the recommendation reason. However, generating recommendation reason from product knowledge doesn't naturally respond to users' interests. Fortunately, on some E-commerce websites, there exists more and more user-generated content (user-content for short), i.e., product question-answering (QA) discussions, which reflect user-cared aspects. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. In reality, adequate user-content is only possible for the most popular commodities, whereas large sums of long-tail products or new products cannot gather a sufficient number of user-content. To tackle this problem, we propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests with a posterior multiple QA discussions module, and generating recommendation reasons containing the product attributes as well as the user-cared aspects. Experimental results show that our model is superior to traditional generative models. Additionally, the analysis also shows that our model can focus more on the user-cared aspects than baselines.

E-commerce sites usually leverage taxonomies for better organizing products. The fine-grained categories, regarding the leaf categories in taxonomies, are defined by the most descriptive and specific words of products. Fine-grained product categorization remains challenging, due to blurred concepts of fine grained categories (i.e. multiple equivalent or synonymous categories), instable category vocabulary (i.e. the emerging new products and the evolving language habits), and lack of labelled data. To address these issues, we proposes a novel Neural Product Categorization model---NPC to identify fine-grained categories from the product content. NPC is equipped with a character-level convolutional embedding layer to learn the compositional word representations, and a spiral residual layer to extract the word context annotations capturing complex long range dependencies and structural information. To perform categorization beyond predefined categories, NPC categorizes a product by jointly recognizing categories from the product content and predicting categories from predefined category vocabularies. Furthermore, to avoid extensive human labors, NPC is able to adapt to weak labels, generated by mining the search logs, where the customers' behaviors naturally connect products with categories. Extensive experiments performed on a real e-commerce platform datasets illustrate the effectiveness of the proposed models.

Query intent understanding is a fundamental and essential task in searching, which promotes personalized retrieval results and users' satisfaction. In E-commerce, query understanding is particularly referring to bridging the gap between query representations and product representations. In this paper, we aim to map the queries into the predefined tens of thousands of fine-grained categories extracted from the product descriptions. The problem is very challenging in several aspects. First, a query may be related to multiple categories and to identify all the best matching categories could eventually drive the search engine for high recall and diversity. Second, the same query may have dynamic intents under various scenarios and there is a need to distinguish the differences to promote accurate categories of products. Third, the tail queries are particularly difficult for understanding due to noise and lack of customer feedback information. To better understand the queries, we firstly conduct analysis on the search queries and behaviors in the E-commerce domain and identified the uniqueness of our problem (e.g. longer sessions). Then we propose a Dynamic Product-aware Hierarchical Attention (DPHA) framework to capture the explicit and implied meanings of a query given its context information in the session. Specifically, DPHA automatically learns the bidirectional query-level and self-attentional session-level representations which can capture both complex long range dependencies and structural information. Extensive experimental results on a real E-commerce query data set demonstrate the effectiveness of the proposed DPHA compared to the state-of-art baselines.

Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao, Dawei Yin. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses.In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation.This method can not only match the relevant facts for the input utterance but diffuse them to similar entities.With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base.Our empirical study on a real-world dataset proves that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.The experiment results also show that our model outperforms competitive baseline models significantly.

The task of dialogue generation aims to automatically provide responses given previous utterances. Tracking dialogue states is an important ingredient in dialogue generation for estimating users' intention. However, the expensive nature of state labeling and the weak interpretability make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.

Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.

Dialogue systems help various real applications interact with humans in an intelligent natural way. In dialogue systems, the task of dialogue generation aims to generate utterances given previous utterances as contexts. Among various spectrums of dialogue generation approaches, end-to-end neural generation models have received an increase of attention. These end-to-end neural generation models are capable of generating natural-sounding sentences with a unified neural encoder-decoder network structure. The end-to-end structure sequentially encodes each word in an input context and generates the response word-by-word deterministically during decoding. However, lack of variation and limited ability in capturing long-term dependencies between utterances still challenge existing approaches. In this paper, we propose a novel hierarchical variational memory network (HVMN), by adding the hierarchical structure and the variational memory network into a neural encoder-decoder network. By emulating human-to-human dialogues, our proposed method can capture both the high-level abstract variations and long-term memories during dialogue tracking, which enables the random access of relevant dialogue histories. Extensive experiments conducted on three large real-world datasets verify a significant improvement of our proposed model against state-of-the-art baselines for dialogue generation.

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models.Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem.In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multiview learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering.Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank.