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Collecting, Storing, Coding, and Analyzing Spoken Tutorial Dialogue Corpora

Explore techniques and tools for managing spoken tutorial dialogue data, from collection to analysis, using advanced coding methods and open-source software. Learn about data storage formats, coding systems, and analysis tools for in-depth examination.

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Collecting, Storing, Coding, and Analyzing Spoken Tutorial Dialogue Corpora

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  1. Collecting, Storing, Coding, and Analyzing Spoken Tutorial Dialogue Corpora Diane Litman LRDC & Pitt CS

  2. ITSPOKE Tutorial Dialogue Corpora • Students engage in spoken dialogue with tutors, in the qualitative physics domain • human tutors • (fully automated) computer tutors • ‘wizard’ computer tutors

  3. Data Collection • Speech-enhanced computer interfaces • Head-mounted microphones • Currently no video • Humans can be at different locations • Human and Wizard Tutoring • Dialogue speech files • Computer Tutoring • Utterance speech files • Coordinated system logs

  4. Data Storage • Wav, raw audio, ogg formats • Sampling • 16k samples per second • 16 bits per sample • Stereo (dialogue level) and mono (utterance level)

  5. Coding and Analysis • Initially WaveSurfer • Open Source tool for sound visualization and manipulation • Speech/sound analysis • Sound annotation and transcription • Praat is similar • Recently moved to NXT (NITE XML Toolkit) • Also Open Source • http://groups.inf.ed.ac.uk/nxt/

  6. NXT • Mature open-source libraries to support heavily annotated corpora whether they be multimodal; textual; monologue; dialogue • A powerful integrated query language • Built in tools for common tasks + Java API for custom tools • Media sync built in • Command line tools for data analysis

  7. NXT meets the ICSI Corpus Jean Carletta and Jonathan Kilgour University of Edinburgh HCRC Language Technology Group

  8. ICSI Meeting Corpus • 75 natural meetings from research groups • close-talking and far-field microphones • orthographic transcription • "speech quality" tags (e.g., emphasis) • dialogue acts • hot spots

  9. The NITE XML Toolkit • library support for data handling and search using a data model that can express both timing and complex structure • multiple file stand-off XML data storage • some standard GUIs, data utilities • library support for writing tailored GUIs

  10. Stand-off XML extract from Bdb001.A.speech-quality.xml <speechquality nite:id="Bdb001.emphasis.16" type="emphasis"> <nite:child href="Bdb001.A.words.xml#id(Bdb001.w.1,342)..id(Bdb001.w.1,344)" /> </speechquality> extract from Bdb001.A.words.xml <w nite:id="Bdb001.w.1,342" starttime="356.39" endtime="" c="W">time</w> <w nite:id="Bdb001.w.1,343" starttime="" endtime="" c="HYPH">-</w> <w nite:id="Bdb001.w.1,344" starttime="" endtime="356.59" c="W">line</w>

  11. Tasks • pre-NXT: up-translation and tokenization • hand annotation (topic segmentation, dialogue acts, extractive summaries, ...) • automatic annotation/indexing by query match

  12. Queries in NXT ($a w):(TEXT($a) ~ /th.*/):: ($s speechquality):($s ^ $a) && ($s@type="emphasis") • Find instances of words starting with “th” • For each find instances of speech quality tags of type "emphasis" that dominate the word • Discard words that are not dominated by at least one such tag Use queries to understand data, verify quality, index.

  13. NXT as Meeting Browser • Browser = display + signal indexing + search • NXT data displays: • synchronize with signal • highlight search results

  14. Issues • Already can't load all the ICSI data at once on some machines • NXT supports display of one meeting at a time but browsing may be over several meetings • Really complicated queries are often too slow for browser response times Key: Pre-indexing of query results, tailored data builds

  15. NXT meets the BEETLE Corpus Johanna Moore’s Group University of Edinburgh

  16. Coding Tutorial Dialogue • Partitioned the dialogue into a set of non-overlapping segments with the following category names: • Content • Dialogue that contains information relevant to the topics in the lessons. • Management • Dialogue that does not contain information relevant to the lesson topics, but deals with the flow of the lesson. • Metacognition • Dialogue that contains the student or tutor’s feeling about his or her understanding of the lesson material or each other. • Social • Dialogue that serves as motivation, encouragement, humor, or establishing rapport.

  17. Coding Student Utts for Sig Events NOVELTY1 ACCURACY2 & CONFIDENCE3 ACCURACY2 & DEPTH4 DEPTH4 INITIATIVE5 • Constructivism / generative learning • Osborne & Wittrock, 1983 • Impasses • Van Lehn, et. al., 2003 • Accountable talk • Wolf, Crosson & Resnick, 2006 • Deep processing / cognitive effort • Thomas & Rohwer, 1993 • Motivated, self-directed learner • Thomas & Rohwer, 1993 • Student produces a lot of new information • Student utts are incorrect or correct w/ low confidence • Student utts are both accurate & deep • Student utts are deep (regardless of accuracy)‏ • High frequency of internally motivated student utts • Consider common theories of effective learning events

  18. Student Utterance Coding • Five major dimensions • Accuracy • Correct, some missing, some errors, incorrect • Signs of “deep” processing or cognitive effort • Present versus absent • Explain/justify/support statement with evidence/reasoning • Summarize or paraphrase • Express relationships or make connections between constructs • Questions or challenges statements from lesson or tutor • Wolf, Crosson & Resnick (2006)‏ • Signs of low confidence • Present versus absent (Bhatt, Evens & Argamon, 2004)‏ • Origin • Externally versus internally motivated • Novelty • Old versus new information

  19. Accountable Talk: utt83a: student: both bulbs A and C will go out because this scenario would act the same as if there was an open circuit Accuracy = Correct; Cognitive Processing = Present utt69: student: A and C will not light up  Accuracy = Correct; Cognitive Processing = Absent Non-Accountable Talk: battery utt122a: student: bulb a will light but b and c won't since b is damaged and breaks the closed path circuit Accuracy = Incorrect; Cognitive Processing = Present Cognitive Effort and Potential Impasse: A C B X Potential Impasse: utt97: student: both would be either dim or not light I would think  Accuracy = Partially Correct; Cognitive Processing = Absent; Signs of Low Confidence = Yes Question: If bulb B is damaged, what do you think will happen to bulbs A and C?

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