Datasets:
sample_id string | source_item_id string | split string | context_scope string | history_variant string | full_history_turn_count int64 | dialogue_history list | reference_teacher_response string | teacher_taxonomy_labels list | source_full_context_word_count int64 |
|---|---|---|---|---|---|---|---|---|---|
demo_001 | ta_v1_English_003_t0014 | demo | full_history | full_context | 10 | [
{
"role": "teacher",
"text": "Hi <STUDENT> - are you there?!"
},
{
"role": "student",
"text": "Hi. Yes, here I am :)"
},
{
"role": "teacher",
"text": "Hi there - good to speak to you! <STUDENT> - can you tell me something about yourself - whatever you like..."
},
{
"role": "s... | It's careers! | [
"Correct Response"
] | 174 |
demo_002 | ta_v1_teacherstudentchat00250_t0053 | demo | full_history | full_context | 31 | [
{
"role": "student",
"text": "hi"
},
{
"role": "teacher",
"text": "Hi <STUDENT>! How are you doing?"
},
{
"role": "student",
"text": "I am okay, what about you. ?"
},
{
"role": "teacher",
"text": "I'm fine, thank you! Is it nearly the end of the academic year for you?"
... | We talked about it last time, because it's past simple, right? The three forms are let-let-let | [
"Bugs"
] | 257 |
demo_003 | ta_v1_teacherstudentchat00251_t0019 | demo | full_history | full_context | 9 | [
{
"role": "student",
"text": "Hi <TEACHER>!"
},
{
"role": "teacher",
"text": "Morning! My laptop is being rubbish this morning - sorry! I'm on my phone again until it recovers"
},
{
"role": "student",
"text": "How are you?"
},
{
"role": "teacher",
"text": "How are you? Di... | It __ __ there for several years | [
"Guided revision / Cue"
] | 137 |
demo_004 | ta_v1_teacherstudentchat00247_t0015 | demo | full_history | full_context | 11 | [
{
"role": "student",
"text": "hi! sorry I will be a bit late today"
},
{
"role": "teacher",
"text": "Hi, ok, no worries"
},
{
"role": "student",
"text": "Hi again"
},
{
"role": "teacher",
"text": "Hi <STUDENT>! How's it going?"
},
{
"role": "student",
"text": ... | PR? Do you mean some marketing? | [
"Clarification Check"
] | 132 |
demo_005 | ta_v1_teacherstudentchat00075_t0010 | demo | full_history | full_context | 8 | [
{
"role": "teacher",
"text": "Hi there <STUDENT>..."
},
{
"role": "student",
"text": "Good afternoon <TEACHER>."
},
{
"role": "teacher",
"text": "OK? Did you a whole IELTS test today?"
},
{
"role": "student",
"text": "I just do Listening , Reading and Writing Task 1. did"... | OK great! (try that sentence again <STUDENT> - think about the grammar...) | [
"Affective/ Social Feedback",
"Try Again"
] | 80 |
TACTBench Demonstration Samples
This repository contains five full-context demonstration examples from TACTBench. It does not contain the TACT training set or the remaining hidden TACTBench evaluation set. The samples use the same full-history representation as the benchmark evaluation and illustrate direct correction, error explanation, guided revision, clarification checking, affective feedback, and retry elicitation.
Data
data/demo.jsonl: five complete benchmark contexts selected from five distinct tutoring conversations.- Each row contains the full visible dialogue history before the target teacher turn, the reference teacher response, and its tutor-strategy label or labels.
These rows are demonstration examples and must not be included in future hidden benchmark scoring. Historical paper results were computed on the original 78-item benchmark before these examples were released.
Schema
sample_id: stable identifier for this demonstration release.source_item_id: provenance identifier in the internal benchmark.split: alwaysdemo.context_scope: alwaysfull_history.history_variant: alwaysfull_context, matching the paper evaluation.full_history_turn_count: number of visible dialogue turns before the target.dialogue_history: ordered teacher and student turns.reference_teacher_response: the authentic next teacher turn.teacher_taxonomy_labels: pedagogical function labels for the response.source_full_context_word_count: descriptive word count across the complete visible dialogue history and reference response.
Source and Terms
The dialogue excerpts are derived from the Teacher-Student Chatroom Corpus version 2 (TSCC v2). TSCC is distributed by the University of Cambridge under its own user agreement for non-commercial research and educational use. This sample repository does not grant rights to the complete TSCC corpus. Researchers who need the source corpus should apply through the official TSCC access form: https://forms.gle/pKc48WMhnySC8zDk9.
Citation
Please cite both the TACT paper and the TSCC v2 paper when using these samples.
@article{yang2026tact,
title = {TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring},
author = {Yang, Dongjie and Lin, Siyan and Shen, Leixian and Sheng, Rui and Qu, Huamin and Chen, Zixin},
year = {2026}
}
@inproceedings{caines2022tsccv2,
title = {The Teacher-Student Chatroom Corpus version 2: More Lessons, New Annotation, Automatic Detection of Sequence Shifts},
author = {Caines, Andrew and Yannakoudakis, Helen and Allen, Helen and Perez-Paredes, Pascual and Byrne, Bill and Buttery, Paula},
year = {2022},
booktitle = {Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning}
}
- Downloads last month
- 45