Articles

AbstractConversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications. Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances. Phrases carry sentiments when they are referred to emotional events …...

AbstractMany automatic evaluation metrics have been proposed to score the overall quality of a response in open-domain dialogue. Generally, the overall quality is comprised of various aspects, such as relevancy, specificity, and empathy, and the importance of each aspect differs according to the task. For instance, specificity is mandatory in …...

AbstractUnsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community. The main advantage of the UNMT lies in its easy collection of required large training text sentences while with only a slightly worse performance than supervised neural machine translation which requires expensive annotated translation pairs …...

AbstractConversational systems are of primary interest in the AI community. Organizations are increasingly using chatbot to provide round-the-clock support and to increase customer engagement. Many commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize user input. These frameworks require …...

AbstractWord embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks. In this paper, we explore the hypothesis that embedding methods can also be effectively used to represent spatial locations. Using a new dataset consisting of the …...

AbstractConversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality datasets is essential. Existing CRS datasets mainly focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. In this paper, we contribute a …...

AbstractUnderstanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks–machine translation and dialogue response generation–as they both use the …...

AbstractDialogue systems using deep learning have achieved generation of fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. To address these shortcomings, we propose a new loss function, an Inverse N-gram loss (INF), which incorporates contextual fluency and …...

AbstractEmotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes …...

AbstractExisting end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB …...

AbstractConventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis …...

AbstractIn online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface. Ideally the conversation can be transparently transferred between different sources of customer support …...

AbstractEnglish verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as …...

AbstractLarge amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of …...

AbstractRecent years have seen big advances in the field of sentence-level quality estimation (QE), largely as a result of using neural-based architectures. However, the majority of these methods work only on the language pair they are trained on and need retraining for new language pairs. This process can prove difficult …...

AbstractOur analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents. Yet, redundancy reduction has not been thoroughly investigated in neural summarization. In this work, we systematically explore and compare different ways to deal with redundancy when summarizing long documents. Specifically, we organize …...

AbstractSlot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify …...

AbstractGenerating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation, but also being able to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns …...

AbstractWe conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality …...

AbstractAs smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased. In this paper, to control the emotional parameters of the speech synthesis according to certain dialogue contents, we construct a news dataset with emotion labels (“positive,” “negative,” or “neutral”) annotated for each sentence. We …...