Classification-and-Ranking Architecture for Response Generation based on Intentions (original) (raw)
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Classification-and-Ranking Architecture Based on Intentions for Response Generation Systems
2008
Existing response generation accounts only concern with generation of words into sentences, either by means of grammar or statistical distribution. While the resulting utterance may be inarguably sophisticated, the impact may be not as forceful. We believe that the design for response generation requires more than grammar rules or some statistical distributions, but more intuitive in the sense that the response robustly satisfies the intention of input utterance. At the same time the response must maintain coherence and ...
GENERIC ARCHITECTURE FOR NATURAL LANGUAGE GENERATION IN SPOKEN HUMAN-COMPUTER DIALOGUE
2000
The human-computer dialogue field is nowadays a rather developed technology and research branch in its own right, but consensus hat not been reached yet with respect to several issues. Out of these, several aspects related to answer generation in spoken natural language are addressed in this paper. First, a modular architecture integrated into a distributed, agent-based dialogue framework and in
Proceedings of the 12th International Conference on Natural Language Generation
Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood (MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.
Natural language generation in dialog systems
Proceedings of the first international conference on Human language technology research - HLT '01, 2001
Recent advances in Automatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach. However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user. The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems. We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.
Towards Robust Online Dialogue Response Generation
ArXiv, 2022
Although pre-trained sequence-to-sequence models have achieved great success in dialogue response generation, chatbots still suffer from generating inconsistent responses in real-world practice, especially in multi-turn settings. We argue that this can be caused by a discrepancy between training and realworld testing. At training time, chatbot generates response with the golden context, while it has to generate based on the context consisting of both user utterances and the model predicted utterances during real-world testing. With the growth of the number of utterances, this discrepancy becomes more serious in the multi-turn settings. In this paper, we propose a hierarchical sampling-based method consisting of both utterance-level sampling and semiutterance-level sampling, to alleviate the discrepancy, which implicitly increases the dialogue coherence. We further adopt reinforcement learning and re-ranking methods to explicitly optimize the dialogue coherence during training and in...
Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
End-to-end generation-based approaches have been investigated and applied in task-oriented dialogue systems. However, in industrial scenarios, existing methods face the bottlenecks of reliability (e.g., domain-inconsistent responses, repetition problem, etc) and efficiency (e.g., long computation time, etc). In this paper, we propose a task-oriented dialogue system via action-level generation. Specifically, we first construct dialogue actions from large-scale dialogues and represent each natural language (NL) response as a sequence of dialogue actions. Further, we train a Sequence-to-Sequence model which takes the dialogue history as the input and outputs a sequence of dialogue actions. The generated dialogue actions are transformed into verbal responses. Experimental results show that our light-weighted method achieves competitive performance, and has the advantage of reliability and efficiency. CCS CONCEPTS • Computing methodologies → Discourse, dialogue and pragmatics.
Generation Models for Spoken Dialogues
2003
The paper discusses what kind of generation model is suitable for spoken dialogue responses. We describe different existing models of generation, and compare them from the point of view of spoken dialogue systems. We introduce a model of generation based on new information focus, and argue that this addresses the communicative requirements of flexible spoken dialogue systems (incrementality, immediacy and interactivity). We discuss the relationship between the dialogue manager and the generator, and what kind of interface they should share. We describe a flexible shallow generation approach which combines template-based generation with a pipeline of distinct processing levels.
Speech Communication, 1997
The current article discusses the problem of appropriate intonation selection in Person-Machine dialogues, such as those expected in intelligent information systems when, for example, information retrieval is required. An approach is proposed which integrates the previously mostly separate paradigms of automatic natural language generation and speech synthesis in a Person-Machine dialogue scenario. The article introduces the two independent basis components adopted in the approach a dialogue model for information retrieval (COR) and a text generation system for German (KOMET-PENMAN)-and develops from these a communicutiue-confext-to-speech system architecture. This system provides for the flexible and context-appropriate selection of intonation patterns. The paper argues that such an approach removes some of the well-known gaps in both text-to-speech and concept-to-speech systems.
User-tailored generation for spoken dialogue: An experiment
2002
Recent work on evaluation of spoken dialogue systems suggests that the information presentation phase of complex dialogues is often the primary contributor to dialogue duration. Therefore, better algorithms are needed for the presentation of complex information in speech. This paper evaluates the effect of a user model on generation for three dialogue strategies: SUMMARY, COMPARE and RECOMMEND. We present results showing that (a) both COMPARE and RECOMMEND strategies are effective; and (b) the user model is useful.
2004
This paper presents construction of a spoken dialogue system using a large-scale spoken dialogue corpus with intention tags. In this system, all of main components, such as speech understanding, dialogue management, and response generation, are constructed with corpus-based methods. An evaluation experiment using a test set has shown that the performance of the corpus-based dialogue system is improved by adding examples.