Papers by Yuanmeng Yan
Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System (2021.acl-long)
Copied to clipboard
| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
| Approach: | They introduce a task, Novel Slot Detection, in the task-oriented dialogue system. |
| Outcome: | The proposed task is based on two public NSD datasets and proposes strong baselines . it aims to identify a sequence of tokens and extract semantic constituents from user queries . |
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for zero-shot cross-domain slot filling do not achieve effective knowledge transfer to the target domain. |
| Approach: | They propose a novel approach based on prototypical contrastive learning and a dynamic label confusion strategy for zero-shot slot filling. |
| Outcome: | The proposed model improves on unseen slots while setting new state-of-the-arts on slot filling task. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods of OOD detection only focus on whether a sample is correctly classified . lack of real OOD examples leads to poor prior knowledge about these unknown intents . |
| Approach: | They propose a supervised contrastive learning objective to minimize intra-class variance . they employ an adversarial augmentation mechanism to obtain pseudo diverse views . |
| Outcome: | The proposed method minimizes intra-class variance by pulling together in-domain intents belonging to the same class and maximizes inter-class variation by pushing apart samples from different classes. |
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer (2021.acl-long)
Copied to clipboard
| Challenge: | Existing BERT-based pre-trained language models achieve high performance on many downstream tasks, but native derived sentence representations are collapsed and thus poor performance on semantic textual similarity (STS) tasks. |
| Approach: | They propose a framework for self-supervised Sentence Representation Transfer that adopts contrastive learning to fine-tune BERT in an unsupervised way. |
| Outcome: | The proposed framework improves on the BERT-derived representations by 8% on STS datasets and shows robustness in data scarcity scenarios. |
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
| Outcome: | Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete. |
Rethinking the Word-level Quality Estimation for Machine Translation from Human Judgement (2023.findings-acl)
Copied to clipboard
| Challenge: | Word-level Quality Estimation (QE) of Machine Translation aims to detect potential translation errors in the translated sentence without reference. |
| Approach: | They propose to use a human-generated translation judgment to generate a word-level quality estimate (QE) using a translation error rate toolkit to detect translation errors without reference. |
| Outcome: | The proposed dataset is more consistent with human judgment and confirms the effectiveness of the proposed tag-correcting strategies. |
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots (2020.emnlp-main)
Copied to clipboard
| Challenge: | Open-vocabulary slots degrade neural-based slot filling models because they can take on unlimited set of values and have no semantic restriction nor length limit. |
| Approach: | They propose a model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. |
| Outcome: | The proposed method outperforms other models on open-vocabulary slots without deteriorating performance. |
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to learn representations from text often reflect social biases . previous methods rely on pre-specified direction or suffer from unstable training . |
| Approach: | They propose an adversarial disentangled debiasing model to decouple social bias attributes from intermediate representations trained on the main task. |
| Outcome: | The proposed model decouples social bias attributes from intermediate representations trained on the main task. |
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold (2022.naacl-main)
Copied to clipboard
Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
| Challenge: | Existing methods for OOD detection are based on labeled in-domain data . detecting out-of-domain (OOD) or unknown intents is challenging . |
| Approach: | They propose a novel reassigned contrastive learning method to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents. |
| Outcome: | The proposed method is effective for both aspects of overconfidence issues. |
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (2020.acl-main)
Copied to clipboard
| Challenge: | Recent context-aware models for slot tagging have achieved state-of-the-art performance . however, the presence of OOV( out-of vocab) words significantly degrades the performance of these models. |
| Approach: | They propose a knowledge-enhanced slot tagging model to integrate contextual representation of input text and large-scale lexical background knowledge. |
| Outcome: | The proposed model achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets. |
Adversarial Self-Supervised Learning for Out-of-Domain Detection (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents are unsupervised and require extensive labeled data. |
| Approach: | They propose a self-supervised contrastive learning framework to model discriminative semantic features from unlabeled data. |
| Outcome: | The proposed framework outperforms baseline methods on two public benchmark datasets with a statistically significant margin. |
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Abstractive summarization models have achieved impressive results on document summarizing tasks, but their performance on dialogue modeling is poor due to the crude and straight methods for dialogue encoding. |
| Approach: | They propose a model that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generate better summaries. |
| Outcome: | The proposed model outperforms various dialogue summarization approaches and achieves state-of-the-art (SOTA) ROUGE results on a SAMsum dataset. |
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for detecting out-of-domain (OOD) intents rely on manually labeled samples . a strong generative distance-based classifier can detect OOD samples in task-oriented dialog systems . |
| Approach: | They propose a generative distance-based classifier to detect out-of-domain (OOD) intents . they use Gaussian discriminant analysis to avoid over-confidence problems . |
| Outcome: | The proposed method outperforms baseline methods on four benchmark datasets. |