Coarse-to-Fine Dual Encoders are Better Frame Identification Learners (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent efforts to model frame definitions lack sufficient representation learning of definitions or lack efficient frame modeling. |
| Approach: | They propose a frame-target-encoder architecture that uses coarse-to-fine learning to model alignment between frames and targets. |
| Outcome: | The proposed framework outperforms existing models by 0.93 overall scores and 1.53 R@1 without lf. |
Similar Papers
Exploiting Definitions for Frame Identification (2021.eacl-main)
Copied to clipboard
| Challenge: | a frame-semantic parsing task is to determine which frame best captures the meaning of a word or phrase in a sentence. |
| Approach: | They propose a frame identification model that generates representations for frames and lexical units (senses) they evaluate the model on three data sets and show it consistently achieves better performance than previous systems. |
| Outcome: | The proposed model consistently outperforms previous systems on three data sets. |
Do LLMs Encode Frame Semantics? Evidence from Frame Identification (2025.emnlp-main)
Copied to clipboard
| Challenge: | Using the FrameNet lexical resource, we evaluate large language models under prompt-based inference and observe that they can perform frame identification effectively even without explicit supervision. |
| Approach: | They evaluate large language models under prompt-based inference and observe that they encode latent knowledge of frame semantics. |
| Outcome: | The proposed model can generate coherent frame definitions while generalizing well to out-of-domain benchmarks. |
Contrastive Video-Language Learning with Fine-grained Frame Sampling (2022.aacl-main)
Copied to clipboard
| Challenge: | despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck. |
| Approach: | They propose a fine-grained contrastive objective for video frame sampling to improve cross-modal correspondence. |
| Outcome: | The proposed approach achieves state-of-the-art performance on YouCookII with long videos. |
FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining (2026.acl-long)
Copied to clipboard
| Challenge: | Existing audio-language models excel at clip-level understanding but struggle with frame-level tasks. |
| Approach: | They propose a novel training paradigm that advances both clip- and frame-level alignment in CLAP with heterogeneous data. |
| Outcome: | The proposed training paradigm improves both clip- and frame-level alignment in CLAP with heterogeneous data. |
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
Copied to clipboard
| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
| Outcome: | The proposed method could generate more specific definitions compared with state-of-the-art models. |
Combining ELECTRA and Adaptive Graph Encoding for Frame Identification (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies focus on FI tasks, but none have been done on the computational side. |
| Approach: | They propose a new system for Frame Identification based on pre-trained text encoders trained discriminatively and graphs embedding. |
| Outcome: | The proposed system produces state-of-the-art performance over two benchmarks and all possible splits and cleaning procedures used in the literature. |
Robust Frame-Semantic Models with Lexical Unit Trees and Negative Samples (2024.acl-long)
Copied to clipboard
| Challenge: | Using a RoBERTa-based filter, we achieve an F1 score of 0.775, surpassing the previous state-of-the-art solution by +0.012. |
| Approach: | They propose a new prefix tree modification to enable robust support for multi-word lexical units and a RoBERTa-based filter to achieve an F1 score of 0.775. |
| Outcome: | The proposed model achieves an F1 score of 0.775, surpassing the state-of-the-art model by +0.012. |
Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods assume all silver labels are accurate and treat them equally, but distant supervision is noisy–some silver labels more reliable than others. |
| Approach: | They propose a noise-aware contrastive learning approach that leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations. |
| Outcome: | The proposed approach improves relation extraction performance over state-of-the-art methods on several RE benchmarks. |
Fine-Grained Features-based Code Search for Precise Query-Code Matching (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to locate code snippets from databases represent the semantics of code and query by averaging the features of each token and word. |
| Approach: | They propose a fine-grained code search model that consists of a cross-modal encoder, mapping layer and classification layer to capture fine-granular interactions between code and query. |
| Outcome: | The proposed model significantly outperforms existing methods across multiple programming language datasets. |
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal sentence representation learning methods focus on aligning images and text at a coarse level, resulting in cross-modal misalignment bias and intra-modal semantic divergence. |
| Approach: | They propose a dual-level alignment learning framework for multimodal sentence representation learning that promotes cross-modal and intra-modal alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on semantic textual similarity and transfer tasks on semantic similarity, ranking distillation and global intra-modal alignment learning. |