Abstract: A human-machine dialog system is described which has multiple computer-implemented dialog components. A user client delivers output prompts to a human user and receives dialog inputs including speech inputs from the human user. An automatic speech recognition (ASR) engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) engine processes the text words to determine corresponding semantic interpretations. A dialog manager (DM) generates the output prompts and responds to the semantic interpretations so as to manage a dialog process with the human user. The dialog components share context information with each other using a common context sharing mechanism such that the operation of each dialog component reflects available context information.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: An automatic conversational system has multiple computer-implemented dialogue components for conducting an automated dialogue process with a human user. A user client delivers dialogue output prompts to the human user and receives dialogue input responses from the human user including speech inputs. An automatic speech recognition engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) processing arrangement processes the dialogue input responses and the text words to determine corresponding semantic interpretations. The NLU processing arrangement includes an anaphora processor that accesses different information sources characterizing dialogue context, linguistic features, and NLU features to identify unresolved anaphora in the text words needing resolution in order to determine a semantic interpretation. A dialogue manager manages the dialogue process with the human user based on the semantic interpretations.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: An automatic conversational system has multiple computer-implemented dialogue components for conducting an automated dialogue process with a human user. A user client delivers dialogue output prompts to the human user and receives dialogue input responses from the human user including speech inputs. An automatic speech recognition engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) processing arrangement processes the dialogue input responses and the text words to determine corresponding semantic interpretations. The NLU processing arrangement includes an anaphora processor that accesses different information sources characterizing dialogue context, linguistic features, and NLU features to identify unresolved anaphora in the text words needing resolution in order to determine a semantic interpretation. A dialogue manager manages the dialogue process with the human user based on the semantic interpretations.

Abstract: A human-machine dialogue system is described which has multiple computer-implemented dialogue components. A user client delivers output prompts to a human user and receives dialogue inputs including speech inputs from the human user. An automatic speech recognition (ASR) engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) engine processes the text words to determine corresponding semantic interpretations. A dialogue manager (DM) generates the output prompts and responds to the semantic interpretations so as to manage a dialogue process with the human user. The dialogue components share context information with each other using a common context sharing mechanism such that the operation of each dialogue component reflects available context information.