Papers with annotation pipeline

12 papers
Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification (2025.findings-emnlp)

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Challenge: Large language models acquire general knowledge from pretraining but pretraining data contain undesirable social biases which can be perpetuated or even amplified by LLMs.
Approach: They propose an efficient yet effective annotation pipeline to investigate social biases in pretraining data.
Outcome: The proposed pipeline investigates social biases in the pretraining corpus using protected attribute detection and regard classification.
TaeBench: Improving Quality of Toxic Adversarial Examples (2025.naacl-industry)

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Challenge: Existing adversarial examples generate invalid or ambiguous examples that fool the systems into wrong detection.
Approach: They propose an annotation pipeline for quality control of generated toxic adversarial examples (TAE) they use model-based automated annotation and human-based quality verification to assess quality requirements of a TAE dataset.
Outcome: The proposed pipeline can transfer-attack SOTA toxicity content moderation models and services with adversarial training.
Efficient Annotator Reliability Assessment with EffiARA (2025.acl-demo)

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Challenge: Obtaining annotations from experts is ideal, but this expertise is logistically and financially costly.
Approach: They propose an annotation framework that supports the whole annotation pipeline from understanding the resources required for an annotation task to compiling the annotated dataset.
Outcome: The proposed framework improves classification performance through annotator-reliability-based soft-label aggregation and sample weighting, and increases agreement among annotators through removal of identifying and replacing an unreliable annotation.
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories (2024.findings-emnlp)

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Challenge: Existing studies focus on specialized agents designed for particular tasks.
Approach: They propose to scale annotated interaction trajectories and fine-tune LLMs on AgentBank to get a series of agent models, Samoyed.
Outcome: The proposed model can scale to get generalized agent capabilities.
An Investigation of Noise in Morphological Inflection (2023.findings-acl)

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Challenge: Neural morphological inflection systems can be used for languages with very little supervised data, but are often less likely to have clean, goldstandard data.
Approach: They propose an error taxonomy and annotation pipeline for inflection training data and propose a character-level masked language modeling (CMLM) pretraining objective.
Outcome: The proposed pipeline is based on error taxonomy and annotation pipelines for unsupervised morphological paradigm completion.
Emosical: An Emotion-Annotated Musical Theatre Dataset (2024.findings-emnlp)

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Challenge: Emosical provides rich emotion annotations for musical films by inferring the background story of the characters.
Approach: They propose to use a multimodal dataset of musical films to generate annotated emotion tags for each sample by inferring the background story of the characters.
Outcome: The proposed dataset provides rich emotion annotations for musical films by inferring the background story of the characters.
Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums (2022.coling-1)

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Challenge: Existing therapeutic chatbots lack large-scale conversations between clients and trained counselors . prior work has found that social media platforms such as Reddit are used to vent distress and peers are seen to actively respond to such posts.
Approach: They propose to use peer support platforms to scrape conversational data from Reddit to determine whether counselors' responses align with real therapeutic conversations.
Outcome: The proposed method achieved 97% coverage out of 17.3K responses, meaning that out of 16.8K responses labeled with a moderate agreement.
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)

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Challenge: Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability.
Approach: They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands.
Outcome: Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates.
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)

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Challenge: Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises.
Approach: They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications.
Outcome: The proposed benchmarks are based on four different tasks and are designed to maximize diversity.
PoS Tagging, Lemmatization and Dependency Parsing of West Frisian (2022.lrec-1)

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Challenge: a lemmatizer/PoS tagger/dependency parser for west frisian is released as a web app and as . web service.
Approach: They propose a lemmatizer/PoS tagger/dependency parser for West Frisian using a corpus of 44,714 words in 3,126 sentences that were annotated according to the guidelines of Universal Dependencies version 2.
Outcome: The proposed lemmatizer/PoS tagger/dependency parser performs better than the previous version of Oersetter . the current corpus contains 44,714 words in 3,126 sentences .
STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

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Challenge: Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach: They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome: The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.
Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting (2025.emnlp-main)

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Challenge: Existing toxic speech datasets lack demographic context and age data are limited . funk and its subsidiary accounts target users aged 14-29 .
Approach: a german project introduces a large-scale toxic speech dataset annotated for toxicity . the dataset includes 3,024 human-annotated and 30,024 LLM-annnotated comments . researchers used human expertise and state-of-the-art language models to label comments based on toxic keywords .
Outcome: The study combines human expertise with state-of-the-art language models to identify toxic speech categories.

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