COLING2020.md

Speaker-change Aware CRF for Dialogue Act Classification

• 论文链接：https://aclanthology.org/2020.coling-main.40.pdf
• 代码链接：https://bitbucket.org/guokan_shang/da-classification

LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization

• 论文链接：https://aclanthology.org/2020.coling-main.41.pdf
• 代码链接：https://gitlab.cs.uni-duesseldorf.de/general/dsml/lava-public

Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning

• 论文链接：https://aclanthology.org/2020.coling-main.43.pdf
• 代码链接：https://github.com/mortezaro/mtl-disfluency-detection

Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions

• 论文链接：https://aclanthology.org/2020.coling-main.53.pdf
• 代码链接：https://github.com/ljynlp/BCDCN

Graph-Based Knowledge Integration for Question Answering over Dialogue

• 论文链接：https://aclanthology.org/2020.coling-main.219.pdf
• 代码链接：https://github.com/jianliu-ml/dialogMRC

Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure

• 论文链接：https://aclanthology.org/2020.coling-main.238.pdf
• 代码链接：https://github.com/HIT-SCIR/Molweni

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

• 论文链接：https://aclanthology.org/2020.coling-main.362.pdf
• 代码链接：https://github.com/siat-nlp/DDMN

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

• 论文链接：https://aclanthology.org/2020.coling-main.362.pdf
• 代码链接：https://github.com/siat-nlp/DDMN

Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling

• 论文链接：https://aclanthology.org/2020.coling-main.363.pdf
• 代码链接：https://github.com/OpenMT/OpenMT-py

Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems

• 论文链接：https://aclanthology.org/2020.coling-main.368.pdf
• 代码链接：https://github.com/vitouphy/usl_dialogue_metric

A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection

• 论文链接：https://aclanthology.org/2020.coling-main.371.pdf
• 代码链接：https://github.com/nusnlp/CXM

Does Gender Matter? Towards Fairness in Dialogue Systems

• 论文链接：https://aclanthology.org/2020.coling-main.390.pdf
• 代码链接：https://github.com/marsan-ma/chat_corpus

Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue

• 论文链接：https://aclanthology.org/2020.coling-main.391.pdf
• 代码链接：https://go.rutgers.edu/

MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

• 论文链接：https://aclanthology.org/2020.coling-main.393.pdf
• 代码链接：http://github.com/lowerquality/gentle

EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation

• 论文链接：https://aclanthology.org/2020.coling-main.394.pdf
• 代码链接：https://github.com/qtli/EmpDG

A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations

• 论文链接：https://aclanthology.org/2020.coling-main.436.pdf
• 代码链接：https://github.com/huggingface/transformers

Adversarial Learning on the Latent Space for Diverse Dialog Generation

• 论文链接：https://aclanthology.org/2020.coling-main.441.pdf
• 代码链接：https://github.com/vikigenius/conditional_text_generation

Out-of-Task Training for Dialog State Tracking Models

• 论文链接：https://aclanthology.org/2020.coling-main.596.pdf
• 代码链接：https://gitlab.cs.uni-duesseldorf.de/general/dsml/trippy-public

Language Model Transformers as Evaluators for Open-domain Dialogues

• 论文链接：https://aclanthology.org/2020.coling-main.599.pdf
• 代码链接：https://github.com/SmartDataAnalytics/transformers_dialogue_evaluators

Few-shot Pseudo-Labeling for Intent Detection

• 论文链接：https://aclanthology.org/2020.coling-main.438.pdf
• 代码链接：https://github.com/tdopierre/FewShotPseudoLabeling

Improving Spoken Language Understanding by Wisdom of Crowds

• 论文链接：https://aclanthology.org/2020.coling-main.234.pdf
• 代码链接：https://github.com/ahclab/iDST

Neural Networks approaches focused on French Spoken Language Understanding: application to the MEDIA Evaluation Task

• 论文链接：https://aclanthology.org/2020.coling-main.245.pdf
• 代码链接：https://github.com/saharghannay/MEDIA