POST-PROCESSING

Instantly Learning Preference Alignment via In-context DPO (2025.naacl-long)

Challenge:	Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts.
Approach:	They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing .
Outcome:	The proposed method can generate ASR inputs from clean text using a text-to-speech system.

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Feifan Song, Yuxuan Fan, Xin Zhang, Peiyi Wang, Houfeng Wang

Language Models

Challenge:	Presently, mainstream approaches to HPA heavily depend on fine-tuning . however, the huge computational and annotation costs of fine-timing are hard to ignore .
Approach:	They propose a tuning-free approach to HPA using LLMs' decoding . they first rethink the derivation procedures of DPO and build an instant scorer .
Outcome:	The proposed approach outperforms existing methods even with tuning-free baselines and an upgraded scorer.

Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (2021.eacl-main)

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Jens Kaiser, Sinan Kurtyigit, Serge Kotchourko, Dominik Schlechtweg

Sisyphus, a Workflow Manager Designed for Machine Translation and Automatic Speech Recognition (D18-2)

Challenge:	Lexical semantic change detection is a new and innovative research field.
Approach:	They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance.
Outcome:	The proposed models improve on large corpora and diachronic target corpors . the proposed models are compared with existing models in a variety of learning scenarios .

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Jan-Thorsten Peter, Eugen Beck, Hermann Ney

HARE: a Flexible Highlighting Annotator for Ranking and Exploration (D19-3)

Challenge:	Sisyphus is a workflow manager for Python that can be used for large and complicated workflows.
Approach:	Sisyphus is a Python-based workflow manager that can be used to train and test a machine . it maps all jobs to a unique path and can create links bearing descriptive names.
Outcome:	Sisyphus is a Python-based workflow manager that can handle large experiments . it can be used without modification to edit, debug, document the workflow .

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Denis Newman-Griffis, Eric Fosler-Lussier

Challenge:	Using NLP techniques to analyze new information domains is challenging, authors report . authors demonstrate use of HARE to rank documents based on their relevance to mobility .
Approach:	They propose a system for highlighting relevant information in document collections to support ranking and triage.
Outcome:	The proposed system can be used to rank and explore documents in clinical data . it provides tools for post-processing and qualitative analysis for model development and tuning.

From dictations to clinical reports using machine translation (N18-3)

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Gregory Finley, Wael Salloum, Najmeh Sadoughi, Erik Edwards, Amanda Robinson, Nico Axtmann, Michael Brenndoerfer, Mark Miller, David Suendermann-Oeft

Joint Optimization of Tokenization and Downstream Model (2021.findings-acl)

Challenge:	Medical dictation is one of the most common ways to document clinical encounters.
Approach:	They propose a machine callytranslation technique that automates post-processing tasks . they show that it outperforms conventional systems in correcting errors .
Outcome:	The proposed method outperforms conventional systems in many tasks while being much simpler to maintain.

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Tatsuya Hiraoka, Sho Takase, Kei Uchiumi, Atsushi Keyaki, Naoaki Okazaki

Text Segmentation

Challenge:	Existing studies have reported that an appropriate tokenization depends on each downstream task.
Approach:	They propose a method to find an appropriate tokenization to a downstream task by optimizing a tokenizer and a model.
Outcome:	The proposed method improves on text classification and machine translation tasks.

Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)

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Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis

Language Models

Challenge:	Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels.
Approach:	They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences .
Outcome:	The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences .

Generating Questions from Wikidata Triples (2022.lrec-1)

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Kelvin Han, Thiago Castro Ferreira, Claire Gardent

Knowledge Representation

Challenge:	Existing methods for question generation from knowledge bases rely on extensive pre- and post-processing of the input triple.
Approach:	They revisit KBQG using pre training, a new (triple, question) dataset and taking question type into account and provide a more extended KBqg dataset.
Outcome:	The proposed approach outperforms existing methods in a standard and in 'zero-shot' setting.

A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (2024.eacl-short)

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Phillip Schneider, Manuel Klettner, Elena Simperl, Florian Matthes

Scene Graph Parsing as Dependency Parsing (N18-1)

Challenge:	Generating natural language text from graph-structured data is essential for conversational information seeking.
Approach:	They conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples using a WebNLG dataset.
Outcome:	The proposed models improve their ability to generate natural language text from semantic triples using few-shot prompting, post-processing, and efficient fine-tuning techniques.

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Yu-Siang Wang, Chenxi Liu, Xiaohui Zeng, Alan Yuille

Syntactic Text Processing

Challenge:	Recent studies have focused on parsing structured knowledge graphs from textual descriptions.
Approach:	They propose an alternative but equivalent scene graph representation that connects to dependency parses.
Outcome:	The proposed model outperforms best approaches on image retrieval applications.

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use (2026.acl-demo)

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Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, M Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta

Programming Languages in NLP

Challenge:	Clinical trial databases are central to modern oncology research and drug development.
Approach:	FD-NL2SQL is a schema-aware clinical NL2sql assistant for SQLite-based oncology databases . it decomposes a natural-language question into predicate-level sub-questions and synthesizes executable SQL .
Outcome:	FD-NL2SQL synthesizes SQL based on decomposition, retrieved exemplars, and schema . clinical trial databases are central to modern oncology research and drug development .

Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)

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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo

Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)

Challenge:	Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline.
Approach:	They propose a neural aligner for abstract meaning representation that learns node-to-word alignments without relying on pipelines.
Outcome:	The proposed approach improves accuracy and generalization from AMR2.0 to AMR3.0 corpora.

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Yong Cao, Xianzhi Li, Huiwen Liu, Wen Dai, Shuai Chen, Bin Wang, Min Chen, Daniel Hershcovich

SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases (2026.findings-eacl)

Challenge:	Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task.
Approach:	They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision.
Outcome:	The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods .

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AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh Bashirloo, Mona Shahamat Naderi, Heshaam Faili

Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)

Challenge:	Text-to-SQL systems translate natural language questions into executable SQL queries.
Approach:	They propose a schema linking approach that first constructs a graph based on foreign key relations and then uses a single prompt to a lightweight LLM to extract source and destination tables from the user query.
Outcome:	The proposed method outperforms specialized, fine-tuned, and complex multi-step approaches on BIRD and Spider 2.0 benchmarks.

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Xiang Yu, Simon Tannert, Ngoc Thang Vu, Jonas Kuhn

Syntactic Text Processing

Challenge:	Existing methods for decoding dependency trees are 10 times faster than current ones.
Approach:	They propose a graph-based method to tackle a dependency tree linearization task . they propose to solve a Traveling Salesman Problem and combine the solution into a projective tree .
Outcome:	The proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.

BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript (2022.findings-naacl)

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Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen

D2U: Distance-to-Uniform Learning for Out-of-Scope Detection (2022.naacl-main)

Challenge:	a growing number of livestreaming videos provide useful knowledge with exceptional visual demonstrations.
Approach:	They propose a human-annotated corpus for punctuation restoration in livestreaming video transcripts . they show popular natural language processing tools underperform on sentence boundary detection .
Outcome:	The proposed dataset shows that natural language processing tools underperform on sentence boundary detection on livestreaming video transcripts.

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Eyup Yilmaz, Cagri Toraman

Challenge:	Existing methods for out-of-scope (OOS) detection use classifier confidence score, but model cannot infer correctly.
Approach:	They propose a zero-shot post-processing step that exploits the classification confidence score and the shape of the entire output distribution.
Outcome:	The proposed method improves performance when there is no OOS training data and learning procedure when OOS data is available.

COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)

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Qingting Xu, Kaisong Song, Chaoqun Liu, Yangyang Kang, Xiabing Zhou, Jun Lin, Yu Hong

Low-Resource NLP

Challenge:	Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text.
Approach:	They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text.
Outcome:	The proposed method improves performance on three benchmarks.

Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)

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Hamza Harkous, Isabel Groves, Amir Saffari

Enhancing Task-oriented Dialogue Systems with Generative Post-processing Networks (2023.emnlp-main)

Challenge:	End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text.
Approach:	They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain.
Outcome:	The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods.

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Atsumoto Ohashi, Ryuichiro Higashinaka

Natural Language Interfaces

Challenge:	Recent work proposes a method to optimize pipelined dialogue systems by fine-tuning modules directly.
Approach:	They propose a new post-processing component for natural language generation (NLG) they use dialogue act contribution to evaluate contribution of GenPPN-generated utterances .
Outcome:	The proposed method improves the performance of task-oriented dialogue systems by modifying arbitrary modules including non-differentiable ones.

Populating Legal Ontologies using Semantic Role Labeling (2020.lrec-1)

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Llio Humphreys, Guido Boella, Luigi Di Caro, Livio Robaldo, Leon van der Torre, Sepideh Ghanavati, Robert Muthuri

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training (2021.acl-long)

Challenge:	This paper is concerned with the ‘resource consumption bottleneck’ of creating semantic technologies manually.
Approach:	They propose to combine general-purpose NLP modules with pre- and post-processing using rules based on domain knowledge to solve the acquisition paradox.
Outcome:	The proposed system extracts norms from legislation and represents them as structured norms in legal ontologies.

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Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, Albert Y.S. Lam

Sentiment Analysis

Challenge:	Existing methods for out-of-scope intent detection rely on strong assumptions on data distribution and confidence threshold selection.
Approach:	They propose a method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training.
Outcome:	The proposed method improves on four benchmark dialogue datasets and improves over state-of-the-art methods.

Effect of Post-processing on Contextualized Word Representations (2022.coling-1)

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Hassan Sajjad, Firoj Alam, Fahim Dalvi, Nadir Durrani

Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD) (2024.findings-acl)

Challenge:	Post-processing of static embeddings has been shown to improve their performance on both lexical and sequence-level tasks.
Approach:	They standardize individual neuron activations using z-score, min-max normalization, and remove top principal components using the all-but-the-top method.
Outcome:	The proposed method unwraps vital information present in the representations for both lexical and sequence classification tasks.

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Avshalom Manevich, Reut Tsarfaty

Goal-Conditioned DPO: Prioritizing Safety in Misaligned Instructions (2025.naacl-long)

Challenge:	Large Vision-Language Models (LVLMs) often produce object hallucinations due to their reliance on text cues and learned object co-occurrence biases.
Approach:	They propose a language-contrasting decoding algorithm that adjusts LVLM outputs based on LLM confidence levels to mitigate object hallucinations.
Outcome:	The proposed method shows up to %4 improvement in POPE F1 scores and %36 reduction in CHAIR scores on COCO validation set while improving captioning quality scores.

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Joo Bon Maeng, Seongmin Lee, Seokin Seo, Kee-Eung Kim

Responsible & Trustworthy NLP

Challenge:	Existing defense methods focus on aligning the model’s output towards less harmful responses through post-processing or input perturbation.
Approach:	They propose a goal-conditioned direct preference optimization technique which is trained to prioritize the system prompt over the user prompt through goal-conditioning and reduces the average Attack Success Rate (ASR) on a wide variety of jailbreak attacks.
Outcome:	The proposed approach reduces the average Attack Success Rate (ASR) on a wide variety of jailbreak attacks while maintaining general performance.

Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)

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Sihan Yang, Chenhang Cui, Zihao Zhao, Yiyang Zhou, Weilong Yan, Ying Wei, Huaxiu Yao

Responsible & Trustworthy NLP

Challenge:	Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing.
Approach:	They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources .
Outcome:	The proposed approach outperforms existing methods in reducing hallucinations and safety concerns.

Frustratingly Easy Model Ensemble for Abstractive Summarization (D18-1)

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Hayato Kobayashi

Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)

Challenge:	Existing studies on compressing or distilling ensemble models have shown that they increase computational costs and reduce performance.
Approach:	They propose an unsupervised method that combines multiple models by selecting a majority-like output in post-processing.
Outcome:	The proposed method performs better than the current ensemble methods on a news-headline-generation task.

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Yinhao Bai, Zhixin Han, Yuhua Zhao, Hang Gao, Zhuowei Zhang, Xunzhi Wang, Mengting Hu

Sentiment Analysis

Challenge:	Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions .
Approach:	They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions .
Outcome:	The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas.

Fingerprinting LLMs via Prompt Injection (2026.acl-long)

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Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong

Responsible & Trustworthy NLP

Challenge:	Existing provenance detection methods for large language models are infeasible for already published models and compare outputs using hand-crafted or random prompts.
Approach:	They propose a detection framework that constructs fingerprints by exploiting LLMs’ inherent vulnerability to prompt injection.
Outcome:	The proposed framework achieves high true positive rates while keeping false positive rates near zero.

Conceptor-Aided Debiasing of Large Language Models (2023.emnlp-main)

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Li Yifei, Lyle Ungar, João Sedoc

Language Models

Challenge:	Pre-trained large language models reflect inherent social biases of their training corpus.
Approach:	They propose two methods to identify and remove the bias subspace in pre-trained large language models such as BERT and GPT by applying conceptors to a conceptor NOT operation.
Outcome:	The proposed method achieves state-of-the-art (SoTA) debiasing while maintaining LLMs’ performance on the GLUE benchmark.

Enhancing the Ranking Context of Dense Retrieval through Reciprocal Nearest Neighbors (2023.emnlp-main)

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George Zerveas, Navid Rekabsaz, Carsten Eickhoff

Information Retrieval

Challenge:	Sparse annotation poses persistent challenges to training dense retrieval models . despite potential future endeavors to extend annotation, issue of false negatives persists .
Approach:	They propose a method that smooths out the annotation of unlabeled relevant documents . they use reciprocal nearest neighbors to estimate relevance and rerank candidates .
Outcome:	The proposed method reduces the issue of false negatives in contrastive learning by reducing sparsity.

A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)

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Jianzhu Bao, Yuhang He, Yang Sun, Bin Liang, Jiachen Du, Bing Qin, Min Yang, Ruifeng Xu

Reasoning

Challenge:	Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks.
Approach:	They propose a generative framework where expected outputs of AM are framed as a simple target sequence.
Outcome:	The proposed framework achieves state-of-the-art on two AM benchmarks.

Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning (2023.findings-emnlp)

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Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, Andre Martins

Low-Resource NLP

Challenge:	Large language models (LLMs) are a promising avenue for machine translation (MT) however, their effectiveness depends on the choice of few-shot examples and they often require extra post-processing due to overgeneration.
Approach:	They propose a method that incorporates few-shot examples during finetuning to improve performance on MT tasks.
Outcome:	The proposed method outperforms few-shot prompting while eliminating the need for in-context examples.

Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)

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Linyu Fan, Wu Wu Yiheng, Jun Xie, Junhui Li, Fang Kong, Guodong Zhou