Papers by Yanlin Feng
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (N19-1)
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| Challenge: | Unsupervised BWE methods are evaluated on word translation or word similarity tasks. |
| Approach: | They propose a method that learns sentiment-specific word representations for two languages in a common space without cross-lingual supervision. |
| Outcome: | The proposed method outperforms previous unsupervised BWE methods and even supervised Bwe methods on three language pairs for cross-lingual sentiment analysis. |
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)
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| Challenge: | Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale. |
| Approach: | They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
| Outcome: | The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)
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| Challenge: | Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed . |
| Approach: | They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism. |
| Outcome: | The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks. |
PhageBench: Can LLMs Understand Raw Bacteriophage Genomes? (2026.findings-acl)
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| Challenge: | phage genome annotation is a critical component of microbial ecosystems and antibiotics. |
| Approach: | They propose a benchmark to evaluate phage genome understanding by mirroring workflow of bioinformatics experts. |
| Outcome: | The benchmark outperforms baseline models in phage contig identification and host prediction. |
GenomeQA: Benchmarking General Large Language Models for Genome Sequence Understanding (2026.acl-long)
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| Challenge: | Existing benchmarks focus on specialized DNA models trained for sequence prediction or evaluate biological knowledge using text-only questions. |
| Approach: | They propose a benchmark to evaluate general-purpose LLMs on sequence-based genome inference tasks. |
| Outcome: | The proposed benchmark outperforms baseline models on sequence-based genome inference tasks. |
Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing (2023.findings-emnlp)
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| Challenge: | CASENT predicts ultra-fine entities mentioned in text into types with calibrated confidence scores. |
| Approach: | They propose a model that predicts ultra-fine entities with calibrated confidence scores for entity typing. |
| Outcome: | The proposed model outperforms existing models in terms of F1 score and calibration error while achieving 50 times faster inference speed. |
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era (2025.acl-long)
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| Challenge: | Graphs are used for storing open-domain knowledge and domain-specific enterprise data. |
| Approach: | They propose to use property graph views on top of the underlying RDF graph to efficiently query LLMs. |
| Outcome: | The proposed graph views can be efficiently queried by LLMs using Cypher . the proposed graphs have a large schema, overlapping and ambiguous relation types and lack of normalization. |