人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

From Discourse to Logic

Tactile sensing in dexterous robot hands - Review

Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions for objects in images. In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie the language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets - RefCOCO, RefCOCO+, and RefCOCOg, shows the advantages of our methods for both referring expression generation and comprehension.

Modeling Context in Referring Expressions

Correcting phoneme recognition errors in learning word pronunciation through speech interaction

Dialogue breakdown detection is a promising technique in dialogue systems. To promote the research and development of such a technique, we organized a dialogue breakdown detection challenge where the task is to detect a system’s inappropriate utterances that lead to dialogue breakdowns in chat. This paper describes the design, datasets, and evaluation metrics for the challenge as well as the methods and results of the submitted runs of the participants.

The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics

This paper presents a taxonomy of errors in chat-oriented dialogue systems. Compared to human-human conversations and task-oriented dialogues, little is known about the errors made in chat-oriented dialogue systems. Through a data collection of chat dialogues and analyses of dialogue breakdowns, we classified errors and created a taxonomy. Although the proposed taxonomy may not be complete, this paper is the first to present a taxonomy of errors in chat-oriented dialogue systems. We also highlight the difficulty in pinpointing errors in such systems.

/pdf/towards-taxonomy-of-errors-in-chat-oriented-dialogue-systems-599q06jaqu.pdf

Towards Taxonomy of Errors in Chat-oriented Dialogue Systems

A speech recognition apparatus includes a speech input unit that receives input speech, a phoneme recognition unit that recognizes phonemes of the input speech and generates a first phoneme sequence representing corrected speech, a matching unit that matches the first phoneme sequence with a second phoneme sequence representing original speech, and a phoneme correcting unit that corrects phonemes of the second phoneme sequence based on the matching result.

Speech recognition apparatus, speech recognition method, and speech recognition robot

This paper proposes a robot that acquires multimodal information, i.e. auditory, visual, and haptic information, fully autonomous way using its embodiment. We also propose an online algorithm of multimodal categorization based on the acquired multimodal information and words, which are partially given by human users. The proposed framework makes it possible for the robot to learn object concepts naturally in everyday operation in conjunction with a small amount of linguistic information from human users. In order to obtain multimodal information, the robot detects an object on a fla surface. Then the robot grasps and shakes it for gaining haptic and auditory information. For obtaining visual information, the robot uses a hand held small observation table, so that the robot can control the viewpoints for observing the object. As for the multimodal concept formation, the multimodal LDA using Gibbs sampling is extended to the online version in this paper. The proposed algorithms are implemented on a real robot and tested using real everyday objects in order to show validity of the proposed system.

http://www.jp.honda-ri.com/upload/document/entry/20111020/Araki_2011_820_5730.pdf

Kotaro Funakoshi

Papers

Correcting phoneme recognition errors in learning word pronunciation through speech interaction

The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics

Towards Taxonomy of Errors in Chat-oriented Dialogue Systems

Speech recognition apparatus, speech recognition method, and speech recognition robot

Autonomous acquisition of multimodal information for online object concept formation by robots