Abstract: Methods and systems for multimodal collaborative conversational dialogue are disclosed. The multimodal collaborative conversational dialogue system include a multimodal avatar interface and one or more sensors, which obtains one or more multimodal inputs. A multimodal semantic parser generates one or more logical form representations based on the one or more multimodal inputs. A collaborative dialogue manager infers a goal of the user from the one or more logical form representations, and develops a plan including communicative actions and non-communicative actions with regard to the goal. The multimodal avatar interface outputs one or more multimodal collaborative plan-based dialogue system-generated communications with respect to execution of at least one communicative action. The collaborative dialogue manager maintains a collaborative dialogue with the user until obtainment of the goal.

Abstract: Methods and systems for multimodal collaborative conversational dialogue are disclosed. The multimodal collaborative conversational dialogue system include a multimodal avatar interface and one or more sensors, which obtains one or more multimodal inputs. A multimodal semantic parser generates one or more logical form representations based on the one or more multimodal inputs. A collaborative dialogue manager infers a goal of the user from the one or more logical form representations, and develops a plan including communicative actions and non-communicative actions with regard to the goal. The multimodal avatar interface outputs one or more multimodal collaborative plan-based dialogue system-generated communications with respect to execution of at least one communicative action. The collaborative dialogue manager maintains a collaborative dialogue with the user until obtainment of the goal.

Abstract: A system which allows a user to teach a computational device how to perform complex, repetitive tasks that the user usually would perform using the device's graphical user interface (GUI) often but not limited to being a web browser. The system includes software running on a user's computational device. The user “teaches” task steps by inputting natural language and demonstrating actions with the GUI. The system uses a semantic ontology and natural language processing to create an explicit representation of the task that is stored on the computer. After a complete task has been taught, the system is able to automatically execute the task in new situations. Because the task is represented in terms of the ontology and user's intentions, the system is able to adapt to changes in the computer code while still pursuing the objectives taught by the user.

Abstract: A system which allows a user to teach a computational device how to perform complex, repetitive tasks that the user usually would perform using the device's graphical user interface (GUI) often but not limited to being a web browser. The system includes software running on a user's computational device. The user “teaches” task steps by inputting natural language and demonstrating actions with the GUI. The system uses a semantic ontology and natural language processing to create an explicit representation of the task that is stored on the computer. After a complete task has been taught, the system is able to automatically execute the task in new situations. Because the task is represented in terms of the ontology and user's intentions, the system is able to adapt to changes in the computer code while still pursuing the objectives taught by the user.

Abstract: A language model comprising a plurality of augmented-word n-grams and probabilities corresponding to such n-grams. Each n-gram is comprised of a sequence of augmented words. Each augmented word is comprised of the orthographic representation of the word together with a tag representing lexical information regarding the word, such as syntactic or semantic information. Also disclosed are a method of building such a language model, a method of automatically recognizing speech using the language model and a speech recognition system that employs the language model.

Abstract: A language model comprising a plurality of augmented-word n-grams and probabilities corresponding to such n-grams. Each n-gram is comprised of a sequence of augmented words. Each augmented word is comprised of the orthographic representation of the word together with a tag representing lexical information regarding the word, such as syntactic or semantic information. Also disclosed are a method of building such a language model, a method of automatically recognizing speech using the language model and a speech recognition system that employs the language model.

Abstract: A language model comprising a plurality of augmented-word n-grams and probabilities corresponding to such n-grams. Each n-gram is comprised of a sequence of augmented words. Each augmented word is comprised of the orthographic representation of the word together with a tag representing lexical information regarding the word, such as syntactic or semantic information. Also disclosed are a method of building such a language model, a method of automatically recognizing speech using the language model and a speech recognition system that employs the language model.