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Found 33 papers, 18 papers with code
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency
Pre-trained Language Models (PLMs) are the cornerstone of the modern Natural Language Processing (NLP).
Listener’s Social Identity Matters in Personalised Response Generation
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity.
PIPPA: A Partially Synthetic Conversational Dataset
With the emergence of increasingly powerful large language models, there is a burgeoning interest in leveraging these models for casual conversation and role-play applications.
Domain Incremental Lifelong Learning in an Open World
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance.
Long-Tailed Question Answering in an Open World
A retrieve-then-rerank frame is further introduced to select in-context examples, which guild the LM to generate text that express knowledge for QA tasks.
A Survey on Out-of-Distribution Detection in NLP
Out-of-distribution (OOD) detection is essential for the reliable and safe deployment of machine learning systems in the real world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts
Out-of-Domain (OOD) intent detection is vital for practical dialogue systems, and it usually requires considering multi-turn dialogue contexts.
Empathetic Response Generation via Emotion Cause Transition Graph
Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e. g., emotion status) and cognitive factors (e. g., cause of the emotion).
Semi-Supervised Lifelong Language Learning
In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Estimating Soft Labels for Out-of-Domain Intent Detection
Out-of-Domain (OOD) intent detection is important for practical dialog systems.
CDConv: A Benchmark for Contradiction Detection in Chinese Conversations
Dialogue contradiction is a critical issue in open-domain dialogue systems.
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
Continuous QA Learning with Structured Prompts
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across different input samples to improve the model's generalization performance.
Accuracy on In-Domain Samples Matters When Building Out-of-Domain detectors: A Reply to Marek et al. (2021)
We have noticed that Marek et al. (2021) try to re-implement our paper Zheng et al. (2020a) in their work "OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation".
Generative Adversarial Network
Out of Distribution (OOD) Detection
Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis
These models hence are not optimized for dialog-level emotion detection, i. e. to predict the emotion category of a dialog as a whole.
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.
Rethinking and Refining the Distinct Metric
We provide both empirical and theoretical evidence to show that our method effectively removes the biases existing in the original distinct score.
GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection
Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems.
Ranked #1 on
End-To-End Dialogue Modelling
on MULTIWOZ 2.0
Unsupervised Domain Adaptation with Adapter
However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to deployment a whole fine-tuned PrLM for each domain.
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits
Grounded dialogue models generate responses that are grounded on certain concepts.
MMChat: Multi-Modal Chat Dataset on Social Media
To better investigate this issue, we manually annotate 100K dialogues from MMChat and further filter the corpus accordingly, which yields MMChat-hf.
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling.
Diversifying Dialog Generation via Adaptive Label Smoothing
Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature.
Listener's Social Identity Matters in Personalised Response Generation
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity.
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
However, this task is far from well-explored due to the difficulties of rendering a particular style in coherent responses, especially when the target style is embedded only in unpaired texts that cannot be directly used to train the dialogue model.
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
Further, a model-level distillation process is employed to distill a teacher model trained on high-quality paired data to augmented dialogue pairs, thereby preventing dialogue models from being affected by the noise in the augmented data.
A Large-Scale Chinese Short-Text Conversation Dataset
The cleaned dataset and the pre-training models will facilitate the research of short-text conversation modeling.
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights.
Out-of-domain Detection for Natural Language Understanding in Dialog Systems
Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.
Generative Adversarial Network
Natural Language Understanding
+2
Personalized Dialogue Generation with Diversified Traits
In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues.
Quantifying Context Overlap for Training Word Embeddings
Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations.
