Search Results for author:
Found 16 papers, 9 papers with code
Task-Oriented Clustering for Dialogues
A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently.
Learn to Adapt for Generalized Zero-Shot Text Classification
Generalized zero-shot text classification aims to classify textual instances from both previously seen classes and incrementally emerging unseen classes.
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading
Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document.
A Task-oriented Dialog Model with Task-progressive and Policy-aware Pre-training
A global policy consistency task is designed to capture the multi-turn dialog policy sequential relation, and an act-based contrastive learning task is designed to capture similarities among samples with the same dialog policy.
AKEM: Aligning Knowledge Base to Queries with Ensemble Model for Entity Recognition and Linking
This paper presents a novel approach to address the Entity Recognition and Linking Challenge at NLPCC 2015.
An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog System
The errors from DST might misguide the dialog policy, and the system action brings extra difficulties for the DST module.
SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graph
Existing multimodal conversation agents have shown impressive abilities to locate absolute positions or retrieve attributes in simple scenarios, but they fail to perform well when complex relative positions and information alignments are involved, which poses a bottleneck in response quality.
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
Ranked #1 on
SSTOD
on SSD_NAME
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task.
Ranked #3 on
Text Summarization
on SAMSum
Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue
We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue.
Label-Wise Document Pre-Training for Multi-Label Text Classification
A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations.
Ranked #1 on
Multi-Label Text Classification
on AAPD
(Micro F1 metric)
MrMep: Joint Extraction of Multiple Relations and Multiple Entity Pairs Based on Triplet Attention
This paper focuses on how to extract multiple relational facts from unstructured text.
