Papers by Songlin Yang
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs (2022.naacl-main)
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| Challenge: | Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are widely used structured models. |
| Approach: | They use tensor rank decomposition to reduce computational complexities for a subset of FGGs subsuming HMMs and PCFGs. |
| Outcome: | The proposed model performs better on HMM modeling and unsupervised PCFG parsing than previous work. |
Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing (2022.findings-acl)
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| Challenge: | Existing graph-based methods that score dependency trees do not score dependency arcs at all. |
| Approach: | They propose a headed-span-based method that decomposes the score of a dependency tree into scores of headed spans. |
| Outcome: | The proposed method improves over first-order graph-based methods, but does not score dependency arcs at all. |
PUNR: Pre-training with User Behavior Modeling for News Recommendation (2023.findings-emnlp)
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| Challenge: | Existing news recommendation methods use pre-trained language models to produce news vectors and user vectors. |
| Approach: | They propose an unsupervised pre-training paradigm with two tasks for user behavior modeling. |
| Outcome: | The proposed model improves on the real-world news benchmark. |
Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)
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| Challenge: | supervised dependency parsers can reach a very high accuracy, but they require treebanks for training. |
| Approach: | They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. |
| Outcome: | The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset. |
QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition (2023.findings-acl)
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| Challenge: | Experimental results show that multimodal emotion recognition is a state-of-the-art technique . textual, visual and acoustic modalities are involved in multimodal video emotion recognition . |
| Approach: | They propose a quantum-inspired adaptive-priority-learning model to address the challenges . they use quantum state to model modal features and Q-attention to integrate three modalities . |
| Outcome: | Experimental results show that QAP improves on previous models. |
Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols (2021.naacl-main)
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| Challenge: | Recent work shows that probabilistic context-free grammars with neural parameterization can be effective in unsupervised constituency parsing. |
| Approach: | They propose a parameterization form of PCFGs based on tensor decomposition which has at most quadratic computational complexity in the symbol number. |
| Outcome: | The proposed model improves unsupervised constituency parsing performance across ten languages. |
Semantic Dependency Parsing with Edge GNNs (2022.findings-emnlp)
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| Challenge: | Existing semantic dependency parsers use factor graphs to generate a tree structure, but they are ill-suited for a more complex semantic relationship representation. |
| Approach: | They propose a second-order neural CRF parser that uses factor graphs to generate a dependency edge and define neighbors in terms of sibling, co-parent, and grandparent relationships. |
| Outcome: | The proposed model outperforms the first-order biaffine parser on English datasets and shows that it is more efficient than the first order. |
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions (2023.findings-emnlp)
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| Challenge: | Existing approaches to scaling dense PCFGs to thousands of nonterminals have shown to be beneficial for unsupervised parsing, but they still perform poorly as a language model and as an unsupervised model. |
| Approach: | They propose a simple PCFG formalism with independent left and right productions that scales more effectively as a language model and as an unsupervised parser. |
| Outcome: | The proposed formalism scales better as a language model and as an unsupervised parser despite imposing a stronger independence assumption compared to low-rank approaches. |
Don’t Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection (2023.acl-long)
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| Challenge: | Constituency parsing is a fundamental task in natural language processing, having many applications in downstream tasks such as language modeling. |
| Approach: | They propose a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection. |
| Outcome: | The proposed model can predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without expensive chart-based parsing algorithms. |
Structured Mean-Field Variational Inference for Higher-Order Span-Based Semantic Role Labeling (2023.findings-acl)
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| Challenge: | Span-based semantic role labeling is traditionally tackled by BIObased sequence labeling approaches. |
| Approach: | They propose to decompose the edge from predicate word to argument span into three different edges, enabling higher-order inference. |
| Outcome: | The proposed model outperforms vanilla MFVI on span-based semantic role labeling benchmarks. |
Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks (2022.acl-long)
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| Challenge: | Constituency parsing and nested named entity recognition (NER) are similar tasks since they aim to predict a collection of nesting and non-crossing spans. |
| Approach: | They propose a model that uses a pointer network to predict a constituency tree's boundary . constituency parsing is an important task in natural language processing . |
| Outcome: | The proposed model achieves state-of-the-art performance on PTB among all BERT-based models and competitive performance on CTB7 in constituency parsing. |
Neural Bi-Lexicalized PCFG Induction (2021.acl-long)
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| Challenge: | Neural lexicalized PCFGs make strong independence assumption on the generation of the child word and thus bilexical dependencies are ignored. |
| Approach: | They propose an approach to parameterize L-PCFGs without making implausible independence assumptions. |
| Outcome: | The proposed approach improves both running speed and unsupervised parsing performance on the English WSJ dataset. |
Uncertainty-Guided Modal Rebalance for Hateful Memes Detection (2024.acl-long)
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| Challenge: | Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment. |
| Approach: | They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features . |
| Outcome: | The proposed framework produces state-of-the-art performance on four widely-used datasets. |
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing (2022.acl-long)
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| Challenge: | Existing methods to recognize named entities have been criticized for their performance on flat NER but fail to handle nested entities. |
| Approach: | They propose to use a span-based constituency parser to tackle nested NER . they use lexicalized constituency trees to model nesting entities . |
| Outcome: | The proposed method achieves state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive performance on the GENIA platform. |
Chain of Attack: Hide Your Intention through Multi-Turn Interrogation (2025.findings-acl)
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| Challenge: | Existing jailbreak attacks focus on single-turn dialogue scenarios, leaving vulnerabilities in multi-turn contexts inadequately explored. |
| Approach: | They propose an optimal interrogation principle to conceal the jailbreak intent and introduce a multi-turn attack chain generation strategy called CoA. |
| Outcome: | The proposed method shows that black-box LLMs exhibit insufficient resistance under multi-turn interrogation, with more advantages (ASR, 83% vs 64%) |
Improving Span Representation by Efficient Span-Level Attention (2023.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality span representations are limited by subset of tokens . span-span interactions should play an important role in span encoding, authors argue . |
| Approach: | They propose to introduce span-span interactions and more comprehensive span-token interactions to improve span representations. |
| Outcome: | The proposed model outperforms baseline models on span-related tasks and shows superior performance. |
Headed-Span-Based Projective Dependency Parsing (2022.acl-long)
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| Challenge: | Existing methods for dependency parsing based on headed spans are available. |
| Approach: | They propose a method for projective dependency parsing based on headed spans. |
| Outcome: | The proposed method achieves state-of-the-art or competitive results on PTB, CTB, and UD Dependency parsing is an important task in natural language processing. |
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks (2023.emnlp-main)
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| Challenge: | Entity and Relation Extraction (ERE) is an important task in information extraction. |
| Approach: | They propose a hypergraph neural network for ERE built upon the PL-marker . they use a pruner mechanism to transfer the burden of entity identification to the joint module . |
| Outcome: | The proposed model improves on three widely used benchmarks on ERE task . it uses a pruner mechanism to transfer the burden of entity identification to the joint module . |
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information. |
| Approach: | They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public datasets. |
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars (2023.acl-long)
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| Challenge: | a recent study shows that context-free grammars are not natural for modeling discontinuous language phenomena such as extrapositions and cross-serial dependencies. |
| Approach: | They propose a grammar induction approach with mildly context-sensitive grammars for unsupervised discontinuous parsing. |
| Outcome: | Experiments on German and Dutch show that the proposed grammar induction method is beneficial for unsupervised parsing. |