Papers with noisy
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking (2020.starsem-1)
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| Challenge: | Existing methods for dialog state tracking are ontology-based and ontologie-free . however, it is not clear enough which slots are better handled by either of the two methods . |
| Approach: | They propose a dual-strategy model that integrates both ontology-based and ontological-free methods. |
| Outcome: | The proposed model outperforms the existing model on noisy and cleaner datasets. |
LexiClean: An annotation tool for rapid multi-task lexical normalisation (2021.emnlp-demo)
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| Challenge: | Lexical normalisation is the task of identifying and normalising non-canonical tokens (e.g. erroneous spelling, acronyms, etc.) in noisy, non-standard, corpora. |
| Approach: | They propose to use LexiClean to annotate multiple tasks in noisy corpora using in situ token modification and annotation that can be rapidly applied corpus wide. |
| Outcome: | The proposed tool can be rapidly applied corpus wide and can identify and normalise noisy, non-standard, and domain specific corpora. |
A Recorded Debating Dataset (L18-1)
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Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
LMUNIT: Fine-grained Evaluation with Natural Language Unit Tests (2025.findings-emnlp)
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Jon Saad-Falcon, Rajan Pathe Vivek, William Berrios, Nandita Shankar Naik, Matija Franklin, Bertie Vidgen, Amanpreet Singh, Douwe Kiela, Shikib Mehri
| Challenge: | Using natural language unit tests, language models are costly and noisy, and automated metrics provide only coarse, difficult-to-interpret signals. |
| Approach: | They propose a paradigm that decomposes response quality into explicit, testable criteria and a unified scoring model, LMUnit, which combines multi-objective training across preferences, direct ratings, and natural language rationales. |
| Outcome: | The proposed paradigm significantly improves inter-annotator agreement and enables more effective LLM development workflows. |
Fluent Translations from Disfluent Speech in End-to-End Speech Translation (N19-1)
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| Challenge: | Disfluency removal is an intermediate step between speech recognition and machine translation (MT) with the rise of end-to-end speech translation systems, disfluency recognition and removal needs to be incorporated into the model architectures or handled as a post-processing step. |
| Approach: | They propose to use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
| Outcome: | The proposed model generates fluent translations from disfluent speech using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
Efficient PRM Training Data Synthesis via Formal Verification (2026.findings-acl)
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Ryo Kamoi, Yusen Zhang, Nan Zhang, Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, Wenpeng Yin, Rui Zhang
| Challenge: | Existing approaches for constructing PRM training data rely on human annotation or sampling-based labeling methods that require repeated LLM calls. |
| Approach: | They propose a framework that synthesizes PRM training data by annotating step-level error labels using formal verification tools such as Z3 and Isabelle. |
| Outcome: | The proposed framework synthesizes PRM training data from formal logic and theorem proving tasks without human annotation or additional LLM calls. |
Instance-adaptive training with noise-robust losses against noisy labels (2021.emnlp-main)
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| Challenge: | Several noise-robust losses have been proposed and evaluated on tasks in computer vision, but they use a single dataset-wise hyperparamter to control the strength of noise resistance. |
| Approach: | They propose to change single dataset-wise hyperparameters of noise resistance to be instance-wise. |
| Outcome: | The proposed frameworks increase noise-robustness on noisy and corrupted NLP datasets. |
Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods synthesize pseudo data through back translation but lack guidance on target style features. |
| Approach: | They propose a knowledge-augmented stylized dialogue generation model with a feature-guided style knowledge selection module that utilizes context and response features. |
| Outcome: | The proposed model produces a satisfactory performance on two public benchmarks on both semantic and stylized levels. |
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization (2021.emnlp-main)
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| Challenge: | Question Answering models typically use retrieval and reasoning components to identify relevant information for reasoning. |
| Approach: | They propose a retrieval parameterization method that marginalizes unanswerable queries . they show that marginalization allows a model to mitigate false negatives in annotations . |
| Outcome: | The proposed model improves on two multi-document question answering datasets and shows that marginalization improves performance. |
CASSI: Contextual and Semantic Structure-based Interpolation Augmentation for Low-Resource NER (2023.findings-emnlp)
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| Challenge: | Existing methods for text augmentation suffer from annotation corruption for token-level tasks like NER. |
| Approach: | They propose a novel augmentation scheme that generates high-quality contextually diverse augmentations while avoiding annotation corruption. |
| Outcome: | The proposed scheme outperforms existing methods at multiple low resource levels, in multiple languages, and for noisy and clean text. |
Call My Net 2: A New Resource for Speaker Recognition (2020.lrec-1)
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| Challenge: | Call My Net 2 (CMN2) corpus features Tunisian Arabic conversations between friends and family . call recordings include speech in various realistic and natural acoustic settings, both noisy and non-noisy. |
| Approach: | They introduce the Call My Net 2 (CMN2) corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family. |
| Outcome: | The Call My Net 2 (CMN2) corpus contains data from over 400 Tunisian Arabic speakers . each speaker made 10 or more calls each lasting up to 10 minutes . |
MedCoach: Enhancing Medical Reasoning in LLMs via Knowledge Graph-Augmented Chain-of-Thought Distillation (2026.findings-acl)
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| Challenge: | Existing methods for training specialized reasoning models for the medical domain are limited due to the scarcity of high-quality, large-scale Chain-of-Thought (CoT) data. |
| Approach: | They propose a framework that introduces a dedicated coach role to guide the student model through question decomposition. |
| Outcome: | The proposed framework smooths the learning curve in medical reasoning by facilitating domain adaptation before advancing to complex long-chain reasoning. |
Exploring Large Language Models for Detecting Mental Disorders (2025.emnlp-main)
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| Challenge: | Detecting mental disorders and patient emotions through text analysis and machine learning is of increasing interest to researchers over the past decade. |
| Approach: | They compare the performance of traditional machine learning methods and encoder-based models on Russian-language datasets to those of large language models. |
| Outcome: | The proposed models outperform traditional methods on small and noisy datasets, but can perform comparable to language models when trained on patients with clinically confirmed depression. |
Listen, Pause, and Reason: Toward Perception-Grounded Hybrid Reasoning for Audio Understanding (2026.findings-acl)
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| Challenge: | Recent Large Audio Language Models (LALMs) have shown strong capabilities in audio understanding, yet their reasoning remains vulnerable to perceptual errors. |
| Approach: | They propose a large-scale dataset for **Perception-Aware Question Answering** that uses a hierarchical decoupling strategy to separate speech from environmental sounds and distinguishes among multiple speakers. |
| Outcome: | The proposed model improves on MMAU-mini, MMAR, and PAQA while maintaining comparable performance on multiple benchmarks. |