Papers with model analysis
KeywordScape: Visual Document Exploration using Contextualized Keyword Embeddings (2022.emnlp-demos)
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| Challenge: | Existing tools for document visualization assume that keywords have static meanings, but contextualized word embeddings are unrealistic. |
| Approach: | They propose a visual exploration tool that visualizes contextualized word embeddings in documents based on keywords. |
| Outcome: | The proposed tool visualizes keywords in terms of their contextualized embeddings in a semantic landscape that keeps keywords with similar context close to each other, allowing for a more precise search and comparison of documents. |
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
| Approach: | They evaluate CLE models for a large number of language pairs on bilingual lexicon induction and three downstream tasks. |
| Outcome: | The proposed model performance is based on supervised and unsupervised models on bilingual lexicon induction and three downstream tasks. |
Cyclical Contrastive Learning Based on Geodesic for Zero-shot Cross-lingual Spoken Language Understanding (2024.findings-acl)
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| Challenge: | zero-shot cross-lingual SLU is a challenging task in low-resource languages . a lack of labeled training data makes it difficult to align representations of similar sentences . |
| Approach: | They propose a framework that uses cyclical contrastive learning to achieve consistency between languages . they propose to use geodesic to measure the similarity to construct positive and negative pairs . |
| Outcome: | The proposed framework achieves state-of-the-art performance on multiATIS++ and MTOP datasets. |
Punctuations and Predicates in Language Models (2026.findings-eacl)
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| Challenge: | Recent work has shown that LLMs perform tasks in ways that diverge significantly from human reasoning. |
| Approach: | They examine the computational importance of punctuation tokens in large language models . they use zeroing and layer-swapping techniques to examine their necessity and sufficiency . |
| Outcome: | The proposed model differs in GPT-2, DeepSeek, and Gemma in that punctuation is necessary and sufficient in multiple layers . the findings offer new insight into the internal mechanisms of punctuations in LLMs and have implications for interpretability and model analysis. |
Improved Visual Story Generation with Adaptive Context Modeling (2023.findings-acl)
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| Challenge: | a recent work shows that diffusion models generate images of high resolution and semantic consistency to text prompts. |
| Approach: | They propose a method that uses adaptive context modeling to improve leading system . they evaluate their method on pororoSV and FlintstonesSV datasets . |
| Outcome: | The proposed method achieves state-of-the-art FID scores on pororo and Flintstones datasets. |
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
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| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |
RTE-GMoE: A Model-agnostic Approach for Relation Triplet Extraction via Graph-based Mixture-of-Expert Mutual Learning (2025.emnlp-main)
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| Challenge: | Relation Triplet Extraction (RTE) is a fundamental while challenge task in knowledge acquisition. |
| Approach: | They propose a mutual learning framework for Relation Triplet Extraction to address this limitation. |
| Outcome: | The proposed framework improves on four state-of-the-art backbones and benchmarks. |
DECM: Evaluating Bilingual ASR Performance on a Code-switching/mixing Benchmark (2024.lrec-main)
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| Challenge: | Code-switched (CSW) speech is a linguistic phenomenon that occurs when spoken utterances switch languages between sentences. |
| Approach: | They propose to use a dataset to evaluate German-English CSW speech . they show that the dataset includes splits with varying degrees of CSW . |
| Outcome: | The proposed dataset includes spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. |
Visually Grounded Continual Language Learning with Selective Specialization (2023.findings-emnlp)
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| Challenge: | Existing studies on the role of model components in learning specialized or generalizable representations are lacking. |
| Approach: | They propose to analyze selection strategies for visually grounded continual language learning using two diagnostic datasets. |
| Outcome: | The proposed models outperform existing models and provide enough control and flexibility for a thorough model analysis. |
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns (2022.lrec-1)
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| Challenge: | a Python library is available for extracting patterns from textual data. |
| Approach: | They propose a Python library for extracting patterns from textual data . it integrates a public implementation of the existing GrASP algorithm . |
| Outcome: | The proposed library integrates a public implementation of the existing GrASP algorithm. |
Textless Speech Emotion Conversion using Discrete & Decomposed Representations (2022.emnlp-main)
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Felix Kreuk, Adam Polyak, Jade Copet, Eugene Kharitonov, Tu Anh Nguyen, Morgan Rivière, Wei-Ning Hsu, Abdelrahman Mohamed, Emmanuel Dupoux, Yossi Adi
| Challenge: | Existing methods for modifying emotion of speech are difficult because emotion affects all levels simultaneously. |
| Approach: | They propose a method to convert a spoken language speech into a model of emotion . they use phonetic-content units, prosodic features, speaker, and emotion to modify the emotion a speech utterance has. |
| Outcome: | The proposed method beats text-based systems in terms of perceived emotion and audio quality. |
MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
| Approach: | They propose an ASR-Robust SLU framework based on the mixture-of-experts technique to generate additional transcripts from clean transcripts and use it to weigh the representations of the generated transcripts, ASR transcripts . |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark SLU datasets. |