Papers with SOTA model
Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model (2024.emnlp-industry)
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| Challenge: | Existing approaches to named entity recognition are domain specific and require a domain specific architecture. |
| Approach: | They propose a retrieval augmented large language model for Named Entity Recognition . the model uses word-embedding over sentence-level embedding to fine tune . |
| Outcome: | The proposed model outperforms existing models on the CrossNER dataset. |
Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition break the recognition process into several sequential steps. |
| Approach: | They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it . |
| Outcome: | Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model. |
MRL Parsing Without Tears: The Case of Hebrew (2024.findings-acl)
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| Challenge: | a new approach to parsing morphologically rich languages (MRLs) is needed to overcome the deficiencies of current approaches. |
| Approach: | They propose a "flipped pipeline" where multiple layers are predicted independently on whole-token basis and then synthesized. |
| Outcome: | The proposed model achieves near-SOTA performance on Hebrew NLP tasks. |
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference (2022.lrec-1)
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| Challenge: | Natural Language Inference (NLI) is a crucial task in AI and natural language processing. |
| Approach: | They propose an effective transfer learning approach for cross-lingual NLI . they perform experiments on English-Hindi language pairs in cross-linguistic setting . |
| Outcome: | The proposed model improves the baseline model by 10% over the state-of-the-art model. |
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network (D19-1)
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| Challenge: | Existing models for named entity recognition (NER) lack word boundaries information, which is a major barrier to developing a high performance named entity system. |
| Approach: | They propose a Chinese named entity recognition system with word boundaries information . they use word-level representations and character-level models to integrate lexical knowledge into Chinese NER . |
| Outcome: | The proposed model outperforms the state-of-the-art model and achieves a speed of up to 15 times faster than the SOTA model. |
Stacked AMR Parsing with Silver Data (2021.findings-emnlp)
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| Challenge: | Lack of large-scale annotated data is one main challenge for abstract meaning representation (AMR) parsing. |
| Approach: | They propose to use silver data to train a pre-trained abstract meaning representation model. |
| Outcome: | The proposed model outperforms previous models on the AMR2.0 dataset and is faster than the SOTA model. |
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments (2026.acl-long)
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| Challenge: | Existing benchmarks for legal intelligence are limited to static evaluation paradigms or simplified scenarios. |
| Approach: | They introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. |
| Outcome: | The proposed framework assesses task performance and procedural compliance across legal proficiency levels. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
What do we really know about State of the Art NER? (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a well researched task and widely used in real world NLP scenarios. |
| Approach: | They perform a broad evaluation of Named Entity Recognition using a popular dataset that takes into consideration various text genres and sources constituting the dataset at hand. |
| Outcome: | The proposed models perform on a popular dataset and generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context. |
DePlot: One-shot visual language reasoning by plot-to-table translation (2023.findings-acl)
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Fangyu Liu, Julian Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun
| Challenge: | Existing models for visual language reasoning require tens of thousands of training examples and their reasoning capabilities are limited. |
| Approach: | They propose a one-shot solution to visual language reasoning by combining plot-to-text translation and reasoning over the translated text into a modality conversion module. |
| Outcome: | The proposed method improves on human-written queries on plots and charts compared with a fine-tuned SOTA model on human queries. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback (2024.emnlp-main)
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| Challenge: | Recent studies have shown that tool-augmented large language models can interact with external tools in multiple rounds and provide a final answer. |
| Approach: | They propose a tool-augmented large language model that can interact with external tools in multiple rounds and provide a final answer to an instruction. |
| Outcome: | The proposed framework significantly improves Match Rate, Pass Rate and Win Rate by 26.8%, 20.2%, and 5.6% compared to the SOTA model. |
LETS-C: Leveraging Text Embedding for Time Series Classification (2025.acl-long)
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| Challenge: | Recent advances in language modeling have shown promising results when applied to time series data. |
| Approach: | They propose a method to fine-tune large language models for time series classification tasks using text embedding models and a simple classification head. |
| Outcome: | The proposed model outperforms the current SOTA model on a time series classification benchmark and uses only 14.5% of the trainable parameters. |