A Neural Conversational Model Oriol Vinyals (original) (raw)

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.

A Neural Conversational Model for Automatic Generation of Conversations

2018

1Department of Computer Science and Engineering, VJTI, University of Mumbai, India. 2Assistant Professor, Department of Computer Science and Engineering, VJTI, University of Mumbai, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract The conversations between humans and machines is regarded as one of the most hardcore problems in computer technology, which involves interdisciplinary techniques in information retrieval, ML, natural language Understanding and artificial intelligence. Interactive Entity which automatically generate conversations to exchange information smoothly between a people with little knowledge of the computer. Challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Conversational modeling is an important task in natural language understanding and machine learning. This research applies generative models based metho...

Challenging Neural Dialogue Models with Natural Data: Memory Networks Fail on Incremental Phenomena

SEMDIAL 2017 (SaarDial) Workshop on the Semantics and Pragmatics of Dialogue

Natural, spontaneous dialogue proceeds incrementally on a word-byword basis; and it contains many sorts of disfluency such as mid-utterance/sentence hesitations, interruptions, and self-corrections. But training data for machine learning approaches to dialogue processing is often either cleaned-up or wholly synthetic in order to avoid such phenomena. The question then arises of how well systems trained on such clean data generalise to real spontaneous dialogue, or indeed whether they are trainable at all on naturally occurring dialogue data. To answer this question, we created a new corpus called bAbI+ 1 by systematically adding natural spontaneous incremental dialogue phenomena such as restarts and self-corrections to the Facebook AI Research's bAbI dialogues dataset. We then explore the performance of a state-of-theart retrieval model, MemN2N (Bordes et al., 2017; Sukhbaatar et al., 2015), on this more natural dataset. Results show that the semantic accuracy of the MemN2N model drops drastically; and that although it is in principle able to learn to process the constructions in bAbI+, it needs an impractical amount of training data to do so. Finally, we go on to show that an incremental, semantic parser-DyLan-shows 100% semantic accuracy on both bAbI and bAbI+, highlighting the generalisation properties of linguistically informed dialogue models.

GODEL: Large-Scale Pre-Training for Goal-Directed Dialog

arXiv (Cornell University), 2022

We introduce GODEL (Grounded Open Dialogue Language Model), a large pretrained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information external to the current conversation (e.g., a database or document) to produce good responses. Experiments against an array of benchmarks that encompass task-oriented dialog, conversational QA, and grounded open-domain dialog show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot finetuning setups, in terms of both human and automatic evaluation. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses (extrinsic evaluation) in addition to their communicative features (intrinsic evaluation). We show that extrinsic evaluation offers improved inter-annotator agreement and correlation with automated metrics. Code and data processing scripts are publicly available. 1

Modeling Situations in Neural Chat Bots

2017

Social media accumulates vast amounts of online conversations that enable datadriven modeling of chat dialogues. It is, however, still hard to utilize the neural network-based SEQ2SEQ model for dialogue modeling in spite of its acknowledged success in machine translation. The main challenge comes from the high degrees of freedom of outputs (responses). This paper presents neural conversational models that have general mechanisms for handling a variety of situations that affect our responses. Response selection tests on massive dialogue data we have collected from Twitter confirmed the effectiveness of the proposed models with situations derived from utterances, users or time.

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

Proceedings of the AAAI Conference on Artificial Intelligence

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

Towards Deep Conversational Recommendations

ArXiv, 2018

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversat...

Guiding Attention in Sequence-to-Sequence Models for Dialogue Act Prediction

Proceedings of the AAAI Conference on Artificial Intelligence

The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.

Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation of long, informative, coherent, and diverse responses remains a hard task. In this work, we focus on the single turn setting. We add self-attention to the decoder to maintain coherence in longer responses, and we propose a practical approach, called the glimpse-model, for scaling to large datasets. We introduce a stochastic beam-search algorithm with segment-by-segment reranking which lets us inject diversity earlier in the generation process. We trained on a combined data set of over 2.3B conversation messages mined from the web. In human evaluation studies, our method produces longer responses overall, with a higher proportion rated as acceptable and excellent as length increases, compared to baseline sequenceto-sequence models with explicit lengthpromotion. A back-off strategy produces better responses overall, in the full spectrum of lengths. * Both authors contributed equally to this work. † Work done as a member of the Google Brain Residency program (g.co/brainresidency).

A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

2017

Task-oriented dialogue focuses on conversational agents that participate in user-initiated dialogues on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2.