Neural Conversation Model Research Articles

Yes, I am afraid of the sharks and also wild lions!: A multitask framework for enhancing dialogue generation via knowledge and emotion grounding

Current end-to-end neural conversation models inherently lack the capability to generate coherently engaging responses. Efforts to boost informativeness have an adversarial effect on emotional and factual accuracy, as validated by several sequence-based models. While these issues can be alleviated by access to emotion labels and background knowledge, there is no guarantee of relevance and informativeness in the generated responses. In real dialogue corpus, informative words like named entities, and words that carry specific emotions can often be infrequent and hard to model, and one primary challenge of the dialogue system is how to promote the model’s capability of generating high-quality responses with those informative words. Furthermore, earlier approaches depended on straightforward concatenation techniques that lacked robust representation capabilities in order to account for human emotions. To address this problem, we propose a novel multitask hierarchical encoder–decoder model, which can enhance the multi-turn dialogue response generation by incorporating external textual knowledge and relevant emotions. Experimental results on a benchmark dataset indicate that our model is superior over competitive baselines concerning both automatic and human evaluation.

Analysis on Norms of Word Embedding and Hidden Vectors in Neural Conversational Model Based on Encoder-Decoder RNN

Analysis on Norms of Word Embedding and Hidden Vectors in Neural Conversational Model Based on Encoder-Decoder RNN

A Controllable Model of Grounded Response Generation

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity to "hallucinate" facts. While this may be mitigated by access to background knowledge, there is scant guarantee of relevance and informativeness in generated responses. We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. Quantitative and qualitative results show that, using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.

Keyword-Guided Neural Conversational Model

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for tackling this problem is to 1) train a next-turn keyword classifier, and 2) train a keyword-augmented response retrieval model. However, existing approaches in this paradigm have two limitations: 1) the training and evaluation datasets for next-turn keyword classification are directly extracted from conversations without human annotations, thus, they are noisy and have low correlation with human judgements, and 2) during keyword transition, the agents solely rely on the similarities between word embeddings to move closer to the target keyword, which may not reflect how humans converse. In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. Automatic evaluations suggest that commonsense improves the performance of both next-turn keyword prediction and keyword-augmented response retrieval. In addition, both self-play and human evaluations show that our model produces responses with smoother keyword transition and reaches the target keyword faster than competitive baselines.

Chatbot Designing Information Service for New Student Registration Based on AIML and Machine Learning

One of the efforts made by universities to serve prospective students is by providing consulting services and information that is usually carried out directly at the booth provided, through phone service or live chat support available on the college website. Increased visitors will result in waiting times due to limited availability of officers, which results in decreased satisfaction of prospective new students, moreover this service is only available during campus operating hours. One alternative solution to overcome this problem is to use Chatbot, able to answer questions raised by prospective new students which can be categorized as Frequently Asked Questions abbreviated as FAQ. Chatbot technology can be developed with a variety of AI (Artificial Intelligence) techniques. One of them is the AIML (Artificial Intelligence Markup Language) technique. One of the main drawbacks of AIML is that there is no reasoning ability so a learning system that is focused on supervised learning is needed. In the chatbot that will be built the learning process uses a selective neural conversational model or commonly called the Deep Semantic Similarity Model (DSSM) developed by Microsoft. Meanwhile, the measurement of chatbot performance will be done using Confusion Matrix which is a method of evaluating the performance of the algorithm from Machine Learning (ML). The results of the study stated that the chatbot system that was built was able to answer questions posed by prospective students properly and correctly while the questions were available in the chatbot knowledge base.

Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network

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.

Entrainable Neural Conversation Model Based on Reinforcement Learning

The synchronization of words in conversation, called entrainment, is generally observed in human-human conversations. Entrainment has a high correlation with dialogue success, naturalness, and engagement. In this article, we define entrainment scores based on the word similarities in semantic space to evaluate the entrainment of system generation. We optimized a neural conversation model to the entrainment scores using reinforcement learning so that the system can control the degree of entrainment of the system response. Experimental results showed that the proposed entrainable neural conversation model generated comparable or more natural responses than conventional models and satisfactorily controlled the degree of entrainment of the generated responses.

The Neural Network Conversation Model enables the Commonly Asked Student Query Agents

One of the challenges in academic counselling is to provide an automated service system for students. There several query questions asking the faculty staffs about related-academic services each semester. Offered the communication interface more convenience, the novel approach based on neural network model is introduced to investigate the automated conversational agent. The pre-defined dialogue sentences were collected manually from the student query questions and used as the training dataset. The questions have been varied and grouped by topic-categorizing queried from the registration help desk of the department. Artificial intelligence and machine learning have contributed each other to build the conversational agent so-call KUSE-ChatBOT plugged and used in the modern messenger application, LINE. The system is also included the dialogue back-end management system to use in further deep learning model updating. Tensorflow, the machine learning development platform originated by Google, was performed and obtained the learning model using Python development kits. The LINE Messaging APIs is then contributed as the user interface where users could have FAQs' conversation via the LINE application. The KUSE-ChatBOT is outperformed and efficient by providing automated consultation to the students precisely with the accuracy rate over 75 percent. The system could assist the staffs to be able to lessen the workload of answering the same question repeatedly and give response to the student timely.

Exploiting Text Matching Techniques for Knowledge-Grounded Conversation

Knowledge-grounded conversation models aim at generating informative responses for the given dialogue context, based on external knowledge. To generate an informative and context-coherent response, it is important to conjugate dialogue context and external knowledge in a balanced manner. However, existing studies have paid less attention to finding appropriate knowledge sentences from external knowledge sources than to generating proper sentences with correct dialogue acts. In this paper, we propose two knowledge selection strategies: 1) Reduce-Match and 2) Match-Reduce and explore several neural knowledge-grounded conversation models based on each strategy. Models based on Reduce-Match strategy first distill the whole dialogue context into a single vector with salient features preserved and then compare this context vector with the representation of knowledge sentences to predict a relevant knowledge sentence. Models based on Match-Reduce strategy first match every turn of the context with knowledge sentences to capture fine-grained interactions and aggregate them while minimizing information loss to predict the knowledge sentence. Experimental results show that conversation models using each of our knowledge selection strategies outperform the competitive baselines not only in terms of knowledge selection accuracy but also in response generation performance. Our best model based on Match-Reduce outperforms the baselines in the comparative studies with the Wizard of Wikipedia dataset. Also, our best model based on Reduce-Match outperforms them with the CMU Document Grounded Conversations dataset.

An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an endto-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.

A multi-encoder neural conversation model

With the development of deep neural networks, Sequence-to-sequence (Seq2Seq) models become a popular technique of conversation models. Current Seq2Seq models with single encoder-decoder structures tend to generate responses which contain high frequency patterns on datasets. However, these patterns are always generic and meaningless. Generic and meaningless responses will lead the conversation between computer and human to an end quickly. According to our observations, human conversations are always topic related. If the conversation data can be divided into different clusters according to their topics, high frequency patterns will be topic related rather than generic. We consider that a model trained in different clusters can generate more topic related and meaningful responses. Inspired by this idea, we propose a Multi-Encoder Neural Conversation (MENC) model. MENC can make use of topic information by its multi-encoder structure. To the best of our knowledge, it is the first work which applies multi-encoder structures into conversation models. We conduct our experiments on two daily conversation datasets. Our experiments show that MENC gets a better performance than other mainstream models on both subject and object evaluation metrics.

A scaled‐down neural conversational model for chatbots

SummaryDeep learning has revolutionized the field of conversation modeling. A lot of the research has been toward making the conversational agent more human‐like. As a result, overall the model size increases. Bigger models require more data and are costly to build and maintain. Often, for some tasks, high‐quality responses are not necessary. In this paper, a model that consumes fewer resources and a way to augment conversation data without increasing the size of the vocabulary is proposed. The proposed model uses a modified version of the GRU instead of the LSTM to encode and decode sequences of text.

Adversarial training and decoding strategies for end-to-end neural conversation models

This paper presents adversarial training and decoding methods for neural conversation models that can generate natural responses given dialog contexts. In our prior work, we built several end-to-end conversation systems for the 6th Dialog System Technology Challenges (DSTC6) Twitter help-desk dialog task. These systems included novel extensions of sequence adversarial training, example-based response extraction, and Minimum Bayes-Risk based system combination. In DSTC6, our systems achieved the best performance in most objective measures such as BLEU and METEOR scores and decent performance in a subjective measure based on human rating. In this paper, we provide a complete set of our experiments for DSTC6 and further extend the training and decoding strategies more focusing on improving the subjective measure, where we combine responses of three adversarial models. Experimental results demonstrate that the extended methods improve the human rating score and outperform the best score in DSTC6.

Hierarchical Methods for a Unified Approach to Discourse, Domain, and Style in Neural Conversational Models

With the advent of personal assistants such as Siri and Alexa, there has been a renewed focus on dialog systems, specifically open domain conversational agents. Dialog is a challenging problem since it spans multiple conversational turns. To further complicate the problem, there are many contextual cues and valid possible utterances. Dialog is fundamentally a multiscale process given that context is carried from previous utterances in the conversation; however, current neural methods lack the ability to carry human-like conversation. Neural dialog models are based on recurrent neural network Encoder-Decoder sequence-to-sequence models (Sutskever, Vinyals, and Le, 2014; Bahdanau, Cho, and Bengio, 2015). However, these models lack the ability to create temporal and stylistic coherence in conversations. We propose to incorporate dialog acts (such as Statement-non-opinion ["Me, I'm in the legal department."], Acknowledge ["Uh-huh."]) and discourse connectives (e.g. "because," "then"), utterance clustering and domain prediction, and style shifting using hierarchical methods. In particular, we show that clustering of utterance representations automatically allows for a unified hierarchical approach to discourse, domain, and style.

Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models

Dialog response selection is an important step towards natural response generation in conversational agents. Existing work on neural conversational models mainly focuses on offline supervised learning using a large set of context-response pairs. In this paper, we focus on online learning of response selection in retrieval-based dialog systems. We propose a contextual multi-armed bandit model with a nonlinear reward function that uses distributed representation of text for online response selection. A bidirectional LSTM is used to produce the distributed representations of dialog context and responses, which serve as the input to a contextual bandit. In learning the bandit, we propose a customized Thompson sampling method that is applied to a polynomial feature space in approximating the reward. Experimental results on the Ubuntu Dialogue Corpus demonstrate significant performance gains of the proposed method over conventional linear contextual bandits. Moreover, we report encouraging response selection performance of the proposed neural bandit model using the Recall@k metric for a small set of online training samples.

A Knowledge-Grounded Neural Conversation Model

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more useful conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses. We generalize the widely-used Sequence-to-Sequence (Seq2Seq) approach by conditioning responses on both conversation history and external “facts”, allowing the model to be versatile and applicable in an open-domain setting. Our approach yields significant improvements over a competitive Seq2Seq baseline. Human judges found that our outputs are significantly more informative.

Exploring Implicit Feedback for Open Domain Conversation Generation

User feedback can be an effective indicator to the success of the human-robot conversation. However, to avoid to interrupt the online real-time conversation process, explicit feedback is usually gained at the end of a conversation. Alternatively, users' responses usually contain their implicit feedback, such as stance, sentiment, emotion, etc., towards the conversation content or the interlocutors. Therefore, exploring the implicit feedback is a natural way to optimize the conversation generation process. In this paper, we propose a novel reward function which explores the implicit feedback to optimize the future reward of a reinforcement learning based neural conversation model. A simulation strategy is applied to explore the state-action space in training and test. Experimental results show that the proposed approach outperforms the Seq2Seq model and the state-of-the-art reinforcement learning model for conversation generation on automatic and human evaluations on the OpenSubtitles and Twitter datasets.