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

Referring as a Collaborative Process

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

/pdf/a-survey-of-available-corpora-for-building-data-driven-2phruzvgnh.pdf

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.

/pdf/learning-symmetric-collaborative-dialogue-agents-with-2ct1rcan3s.pdf

Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings

We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just $50!”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.

Decoupling Strategy and Generation in Negotiation Dialogues

The starting point for this paper is Ginzburg’s (1995a) observation that the resolvedness conditions for questions are not semantically invariant, but rather “fixed in a particular context to a level identified by the goal” of the conversation (p. 466; see also Clark 1979; Clark & Schober 1992; Perrault & Allen 1980; Groenendijk & Stokhof 1984:II; Gibbs & Bryant 2007). Such goal-orientation is especially clear for questions like Where are you?, in which the appropriate level of granularity and sense of location are so highly variable; each of my office, California, the U.S., Linguistics, and still working on the introduction is resolving in some contexts but non-resolving (even downright unhelpful) in others. One of the chief innovations of models like Ginzburg’s is to bring these goals (issues, plans, decision problems) into the pragmatic model, so that they can play a direct role in resolving underspecification, setting bounds on vagueness, and guiding inferences about the discourse participants’ intentions. This is a leading idea of all the recent task-driven and question-driven models of contextual dynamics (Allen, 1991; Hobbs et al., 1993; Roberts, 1996, 2004; Ginzburg, 1995b, 1996; Groenendijk, 1999; Beaver, 2002; Buring, 2003; Stone et al., 2007; Beaver & Clark, 2008; Groenendijk & Roelofsen, 2009) as well as their game-theoretic and decision-theoretic counterparts (Clark, 1996; Merin, 1997, 1999; Parikh, 2000, 2001; van Rooy, 2003; Benz, 2005; Franke, 2009; Jager, To appear; Frank & Goodman, 2012). The goal of this paper is to develop and motivate general techniques for identifying and characterizing this kind of task dependence in real task-oriented dialogue, where the phenomena can be both quantitatively and qualitatively assessed. The basis for my exploration is the Cards corpus, a large collection of dialogues derived from a two-person collaborative game. The most noteworthy feature of this corpus for present purposes is that its transcripts record not just the dialogue exchanged by the players, but also all their actions in the game world, with high enough fidelity that we can faithfully replay the games in their entirety. Of course, we don’t have access to the players’ beliefs and intentions, but we do have enough linguistic and behavioral cues to make confident inferences about them. My focus is on the corpus’s 800+ answers to the (implicit or explicit) questionWhere are you? Even in the highly constrained Cards world, these answers come at numerous levels of granularity, frommiddle of the board to 2 spaces to the left of the gap underneath the entrance to the middle room. Intuitively, the preferred granularity is governed by the players’ subgoal at that point in the game. For example, where they want to divide up the board for general exploration, very general answers are preferred. When they need to meet or find specific things, only highly specific answers are resolving. After describing the Cards corpus in some detail (sec. 2), I look more closely at the nature of these locative answers (sec. 3), and then begin the process of setting up an experiment to test the hypothesis of goal-orientation. This involves some semantic annotation (sec. 4) and, more interestingly, a technique for interpreting these annotations in the Cards world itself (sec. 5). With these preliminaries in place, it is straightforward to define a precise notion of granularity and show that it correlates with the players’ subgoals (sec. 6). The paper closes with a more precise theoretical explanation for this correlation (sec. 7) and some suggestions for additional experiments along these lines that could be conducted fairly easily with the Cards corpus (sec. 8).

/pdf/goal-driven-answers-in-the-cardsdialogue-corpus-1iw2h1yy7f.pdf

Goal-Driven Answers in the CardsDialogue Corpus

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Christopher Potts

Author Tools

Chat about Author

Papers

Goal-Driven Answers in the CardsDialogue Corpus