350 citations · 437 across the 18 of their papers we have counts for
16 papers · 1 filter
Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilities
Honglin Mu, Yang Xu, Yunlong Feng +4
With the rise of Large Language Models (LLMs), AI assistants' ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accu…
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Bohan Li, Longxu Dou, Yutai Hou +5
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template. This app…
MetaPrompting: Learning to Learn Better Prompts
Yutai Hou, Hongyuan Dong, Xinghao Wang +2
Prompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based ``hard prompts''…
Language Anisotropic Cross-Lingual Model Editing
Yang Xu, Yutai Hou, Wanxiang Che +1
Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific input…
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
Yutai Hou, Cheng Chen, Xianzhen Luo +2
Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples t…
Data Augmentation Approaches in Natural Language Processing: A Survey
Bohan Li, Yutai Hou, Wanxiang Che
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced…
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
Yutai Hou, Yingce Xia, Lijun Wu +6
The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedic…
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling
Yutai Hou, Yongkui Lai, Cheng Chen +2
In this paper, we investigate few-shot joint learning for dialogue language understanding. Most existing few-shot models learn a single task each time with only a few examples. How…
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling
Yutai Hou, Sanyuan Chen, Wanxiang Che +2
Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Clu…
Few-shot Learning for Multi-label Intent Detection
Yutai Hou, Yongkui Lai, Yushan Wu +2
In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevanc…
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding
Yutai Hou, Jiafeng Mao, Yongkui Lai +4
Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such a…
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network
Yutai Hou, Wanxiang Che, Yongkui Lai +4
In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other few-sho…
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Sanyuan Chen, Yutai Hou, Yiming Cui +3
Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning. But such a sequential transfer learning paradigm often confronts t…
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Yutai Hou, Meng Fang, Wanxiang Che +1
Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotate…
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding
Yutai Hou, Zhihan Zhou, Yijia Liu +4
While few-shot classification has been widely explored with similarity based methods, few-shot sequence labeling poses a unique challenge as it also calls for modeling the label de…
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
Yutai Hou, Yijia Liu, Wanxiang Che +1
In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance witho…