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O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)

Challenge:	Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference.
Approach:	They propose to use BERT as the backbone of a non-autoregressive generation model for greatly improved performance.
Outcome:	The proposed model outperforms existing non-autoregressive models and achieves competitive performance with many strong autoregressive model.

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Fenglin Liu, Xuancheng Ren, Xian Wu, Bang Yang, Shen Ge, Xu Sun

Fast Bilingual Grapheme-To-Phoneme Conversion (2022.naacl-industry)

Challenge:	Existing methods for video captioning consider a sequence of frames and biases towards focused objects.
Approach:	They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption .
Outcome:	The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed.

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Hwa-Yeon Kim, Jong-Hwan Kim, Jae-Min Kim

Multilinguality

Challenge:	Autoregressive transformers (ART)-based grapheme-to-phoneme (G2P) models have been proposed for bi/multilingual text-to speech systems.
Approach:	They propose a bilingual grapheme-to-phoneme (G2P) model with autoregressive transformers for fast and exact decoding and data augmentation for predicting output length.
Outcome:	The proposed model achieves better performance than the previous model and 2700% faster inference speed.

Semi-Autoregressive Neural Machine Translation (D18-1)

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Chunqi Wang, Ji Zhang, Haiqing Chen

DiffusPoll: Conditional Text Diffusion Model for Poll Generation (2024.findings-acl)

Challenge:	Existing approaches to neural machine translation are typically autoregressive but suffer from low parallelizability and thus slow at decoding long sequences.
Approach:	They propose a semi-autoregressive Transformer model for fast sequence generation that keeps the autoregressive property in global but relieves in local .
Outcome:	The proposed model achieves 5.58 speedup while maintaining 88% translation quality, significantly better than previous non-autoregressive methods.

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Le Cheng, Shuangyin Li

Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation (2020.emnlp-main)

Challenge:	Social media platforms manipulate public opinion through sheer numbers and cause biases, authors say . they say new paradigm for poll generation can generate high-quality samples while preserving diversity .
Approach:	They propose a non-autoregressive diffusion model that uses masks to generate polls . they use attribute tags to enhance the quality of polls and to diversify poll options .
Outcome:	The proposed model matches the Transformer model while offering greater diversity and quality.

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Jason Lee, Raphael Shu, Kyunghyun Cho

A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Any Translation (2024.acl-long)

Challenge:	Existing non-autoregressive inference procedures that refine in token space often require computational overhead.
Approach:	They propose an efficient inference procedure that iteratively refines translation purely in the continuous space using a latent variable instead of the latent variables.
Outcome:	The proposed procedure is twice as efficient and more effective than the existing EM-like inference procedure.

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Zhengrui Ma, Qingkai Fang, Shaolei Zhang, Shoutao Guo, Yang Feng, Min Zhang

GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding (2023.acl-long)

Challenge:	Existing translation pipelines require additional cascade components to achieve speech-to-speech translation.
Approach:	They propose a non-autoregressive generation framework for simultaneous speech translation . it integrates both text-to-text and speech-tospeech tasks into a unified framework .
Outcome:	The proposed framework outperforms state-of-the-art models in speech-to-text and speech- to-speech tasks.

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Konstantin Yakovlev, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya

Syntactic Text Processing

Challenge:	grammatical error correction is an important NLP task that is usually solved with autoregressive sequence-to-sequence models.
Approach:	They propose a non-autoregressive approach to grammatical error correction that decouples a permutation network and a decoder network that fills in specific tokens.
Outcome:	The proposed approach improves over previously known non-autoregressive methods and reaches the level of autoregressive approaches that do not use language-specific synthetic data generation methods.

Pretrained Models for Multilingual Federated Learning (2022.naacl-main)

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Orion Weller, Marc Marone, Vladimir Braverman, Dawn Lawrie, Benjamin Van Durme

Multilinguality

Challenge:	Federated Learning (FL) is a machine learning technique that trains a model across multiple distributed clients holding local data samples, without ever storing client data in a central location.
Approach:	They propose to use pretrained models to study three multilingual language tasks . they also examine impact of non-IID text on FL in naturally occurring data .
Outcome:	The proposed methods perform better than centralized learning even when using non-IID partitioning.

Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)

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Ye Liu, Yao Wan, Jianguo Zhang, Wenting Zhao, Philip Yu

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer (2021.findings-acl)

Challenge:	Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts.
Approach:	They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation.
Outcome:	The proposed model achieves faster speed and keeps translation quality compared with other models.

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Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

Low-Resource NLP

Challenge:	Autoregressive text style transfer models often ignore part of the source sentence and generate some irrelevant words with strong styles.
Approach:	They propose a non-autoregressive generator for unsupervised text style transfer which explicitly models word alignments to suppress irrelevant words.
Outcome:	The proposed generator significantly improves performance and provides explainable word alignments.

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (2020.emnlp-main)

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Di Wu, Liang Ding, Fan Lu, Jian Xie

Sentiment Analysis

Challenge:	Slot filling and intent detection are two main tasks in spoken language understanding systems.
Approach:	They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem.
Outcome:	The proposed model significantly outperforms previous models in slot filling task while speeding up decoding.

Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment (2021.naacl-main)

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Ethan A. Chi, Julian Salazar, Katrin Kirchhoff

Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing (2021.findings-emnlp)

Challenge:	Non-autoregressive encoder-decoder models improve decoding speed, but generation quality suffers . editing at the level of output sequences limits model flexibility.
Approach:	They propose iterative realignment which iteratively realigns connectionist temporal alignments.
Outcome:	The proposed model matches an autoregressive baseline with a 14x speedup on the WSJ dataset; on LibriSpeech, it achieves an LM-free test-other WER of 9.0% (19% relative improvement on comparable work).

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Akshat Shrivastava, Pierce Chuang, Arun Babu, Shrey Desai, Abhinav Arora, Alexander Zotov, Ahmed Aly

Semantic Parsing

Challenge:	a novel approach to map utterances to semantic frames is based on non-autoregressive parsers that shift the decoding task from text generation to span prediction.
Approach:	They propose a non-autoregressive, task-oriented parser which shifts the decoding task from text generation to span prediction and produces endpoints as opposed to text.
Outcome:	The proposed model bridges the quality gap between non-autoregressive and autoregressive parsers, achieving 87 EM on TOPv2 and shows a 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-regressives.

Non-Autoregressive Math Word Problem Solver with Unified Tree Structure (2023.emnlp-main)

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Yi Bin, Mengqun Han, Wenhao Shi, Lei Wang, Yang Yang, See-Kiong Ng, Heng Shen

Numerical Reasoning

Challenge:	Existing MWP solvers do not handle variants that can be derived via mathematical manipulation.
Approach:	They propose a non-autoregressive solver to present a solution expression and decode it from a given problem description.
Outcome:	The proposed solver is able to decode multiple expression variants and correct them . it is based on a unified tree structure and is available on Math23K and MAWPS.

Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog (2021.naacl-main)

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Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad

ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation (2020.acl-main)

Challenge:	Semantic parsing using sequence-to-sequence models is stymied by higher compute requirements and higher latency.
Approach:	They propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture.
Outcome:	The proposed architecture achieves an 81% reduction in latency on TOP dataset and retains competitive performance over non-pretrained models on three different semantic parsing datasets.

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Lifu Tu, Richard Yuanzhe Pang, Sam Wiseman, Kevin Gimpel

DART: Distilling Autoregressive Reasoning to Silent Thought (2025.emnlp-main)

Challenge:	a non-autoregressive machine translation model can minimize the autoregressive teacher's energy . engINE is an inference network trained to minimize the teacher' energy based on distilled corpora .
Approach:	They propose to train a non-autoregressive machine translation model to minimize autoregressive teacher energy by using an inference network instead of distilled corpora.
Outcome:	The proposed model achieves state-of-the-art non-autoregressive results on two datasets . the proposed model is trained to minimize the autoregressive teacher energy .

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Nan Jiang, Ziming Wu, De-Chuan Zhan, Fuming Lai, Shaobing Lian

Diffusion Glancing Transformer for Parallel Sequence-to-Sequence Learning (2024.naacl-long)

Challenge:	Existing models that use Chain-of-Thought (CoT) have been slow to deploy in real-time applications due to its autoregressive nature.
Approach:	They propose a framework that replaces autoregressive CoT with non-autoregressive Silent Thought (ST) the framework uses a lightweight Reasoning Evolvement Module to align hidden states with the CoT pathway and a Reasoning Embedment Module (REM) during inference, only the ST pathway is activated, enabling the ST tokens to evolve into informative embeddings.
Outcome:	The proposed framework replaces autoregressive CoT with non-autoregressive Silent Thought (ST) it enables LLMs to generate answers directly from ST tokens without additional computational cost .

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Lihua Qian, Mingxuan Wang, Yang Liu, Hao Zhou

Insertion-based Tree Decoding (2021.findings-acl)

Challenge:	Experimental results show that non-autoregressive generation models are superior in generation efficiency but inferior in generation quality.
Approach:	They propose a diffusion glancing transformer which employs a modality diffusion process and residual glancy sampling to improve multi-modality modeling.
Outcome:	The proposed model outperforms autoregressive and non-autoregressive models on machine translation and text generation benchmarks.

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Denis Lukovnikov, Asja Fischer

A Set Prediction Network For Extractive Summarization (2023.findings-acl)

Challenge:	Existing non-autoregressive decoders that are sub-linear can speed up inference for longer sequences.
Approach:	They propose a sub-linear nonautoregressive tree decoder that uses tree-based insertion operations to generate trees in sub-lines . they evaluate their approach on semantic parsing and compare it against strong baselines .
Outcome:	The proposed approach achieves competitive accuracies while reducing the number of decoding steps.

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Xiaoxia Cheng, Yongliang Shen, Weiming Lu

Empirical Analysis of Decoding Biases in Masked Diffusion Models (2026.acl-long)

Challenge:	Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences.
Approach:	They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel .
Outcome:	The proposed method outperforms previous state-of-the-art models on extracted summary datasets.

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Pengcheng Huang, Tianming Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Tong Xiao, Zulong Chen, Maosong Sun

Non-autoregressive Streaming Transformer for Simultaneous Translation (2023.emnlp-main)

Challenge:	Existing MDMs employ uncertainty-based decoding strategies that limit their reasoning ability and ultimately degrade generation quality.
Approach:	They propose a framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness.
Outcome:	The proposed framework outperforms existing decoding strategies by more than 7% while achieving comparable performance to autoregressive models of similar parameter scales.

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Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, Yang Feng

A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)

Challenge:	Simultaneous machine translation models are trained to strike a balance between latency and translation quality.
Approach:	They propose a non-autoregressive streaming Transformer which generates blank tokens and decodes repetitive tokens to adjust its READ/WRITE strategy flexibly.
Outcome:	The proposed model outperforms previous strong autoregressive models on various benchmarks on siMT.

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Sweta Agrawal, Weijia Xu, Marine Carpuat

End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (D18-1)

Challenge:	Existing models that generate generic simplified outputs for a given source text have been used to specify output properties.
Approach:	They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity.
Outcome:	The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting.

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Jindřich Libovický, Jindřich Helcl

Information Retrieval

Challenge:	Autoregressive decoding is the only part of sequence-to-sequence models that prevents massive parallelization at inference time.
Approach:	They propose a non-autoregressive architecture based on connectionist temporal classification . they conduct experiments on the WMT English-Romanian and English-German datasets .
Outcome:	The proposed model achieves a significant speedup over autoregressive models . the model can be trained end-to-end and maintains translation quality comparable to other models compared to autoregression models based on connectionist temporal classification .

Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification (2024.lrec-main)

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Hao Yu, Kaiyu Huang, Anqi Zhao, Junpeng Liu, Degen Huang

Information Retrieval

Challenge:	Existing studies employ autoregressive translation (AT) methods to encode sentences . however, the AT methods struggle with error accumulation when the length of sentences increases.
Approach:	They propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification loss for document-level neural machine translation.
Outcome:	The proposed framework achieves 46X speedup on three benchmarks compared to strong baselines.

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)

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Peng Wang, Junyang Lin, An Yang, Chang Zhou, Yichang Zhang, Jingren Zhou, Hongxia Yang

Structured Data in NLP

Challenge:	Existing methods for table-to-text generation suffer from poor faithfulness and low coverage.
Approach:	They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model.
Outcome:	The proposed method outperforms the existing methods on WikiPerson and WikiBio datasets on coverage and faithfulness.

Non-autoregressive Text Editing with Copy-aware Latent Alignments (2023.emnlp-main)

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Yu Zhang, Yue Zhang, Leyang Cui, Guohong Fu

Identifying Cellular Niches in Spatial Transcriptomics: An Investigation into the Capabilities of Large Language Models (2025.acl-long)

Challenge:	Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages.
Approach:	They propose a non-autoregressive text editing method that models the edit process with latent CTC alignments and introduces the copy operation into the edit space.
Outcome:	The proposed method outperforms existing Seq2Edit models and achieves similar or even better results than Seq1Edit with over 4 speedup.

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Huanhuan Wei, Xiao Luo, Hongyi Yu, Jinping Liang, Luning Yang, Lixing Lin, Alexandra Popa, Xiting Yan

Responsible & Trustworthy NLP

Challenge:	Spatial transcriptomic technologies allow measuring gene expression profile and spatial information of cells in tissues simultaneously.
Approach:	They propose a spatial transcriptomic approach to identify spatial niches using a zero-shot large language models by transforming spatial transcriptomics data into spatial context prompts.
Outcome:	The proposed model improves performance by leveraging gene expression of neighboring cells/spots, cell type composition, tissue information, and external knowledge.

Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)

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Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, Jiajun Chen

POS-Constrained Parallel Decoding for Non-autoregressive Generation (2021.acl-long)

Challenge:	Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables .
Approach:	They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs .
Outcome:	The proposed model achieves comparable or better performance in machine translation tasks than strong baselines.

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Kexin Yang, Wenqiang Lei, Dayiheng Liu, Weizhen Qi, Jiancheng Lv

Composing Ci with Reinforced Non-autoregressive Text Generation (2022.emnlp-main)

Challenge:	Existing non-autoregressive generation systems face multimodality problem due to conditionally independent decoding.
Approach:	They propose to incorporate linguistic structure into NAG inference instead of teacher AG . they propose a method that provides a specific POS sequence to constrain the NAG model .
Outcome:	The proposed method improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation.

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Yan Song

Mirror: A Universal Framework for Various Information Extraction Tasks (2023.emnlp-main)

Challenge:	Existing approaches to compose Ci are limited in handling the constraints of tune patterns . authors propose a non-autoregressive approach to generate Ci using a synchronous process .
Approach:	They propose to compose Ci using a non-autoregressive approach that takes into account rigid formats . they propose to apply reinforcement learning to the generation process with rigid constraints .
Outcome:	The proposed method outperforms baselines and previous studies on a Ci dataset . it allows the model to perform synchronous generation while maintaining the format and content requirement.

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Tong Zhu, Junfei Ren, Zijian Yu, Mengsong Wu, Guoliang Zhang, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Min Zhang

OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification (2024.acl-long)

Challenge:	Recent studies often formulate IE tasks as a triplet extraction problem, but this paradigm does not support multi-span and n-ary extraction, leading to weak versatility.
Approach:	They propose a multi-span cyclic graph extraction problem and a non-autoregressive graph decoding algorithm to extract all spans in a single step.
Outcome:	The proposed model outperforms or reaches competitive performance with SOTA systems under few-shot and zero-shot settings and it is compatible with 57 datasets.

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Yifan Peng, Yui Sudo, Muhammad Shakeel, Shinji Watanabe

JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)

Challenge:	Autoregressive models can be slower during inference and have potential risks of hallucination.
Approach:	They propose an encoder-only speech foundation model based on Connectionist Temporal Classification.
Outcome:	The proposed model improves on 180k hours of public audio data for multilingual speech recognition, speech translation, and language identification.

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Xiaobo Liang, Lijun Wu, Juntao Li, Min Zhang

Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing (2026.acl-long)

Challenge:	Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners.
Approach:	They propose a joint autoregressive and non-autoregressive training method using aUxiliary losS to enhance the model performance in both AR and NAR manners simultaneously.
Outcome:	The proposed method improves the model performance in both AR and NAR manners and reduces the inference latency.

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Lingkun Long, Yushi Huang, Shihao Bai, Ruihao Gong, Jun Zhang, Ao Zhou, Jianlei Yang

Green & Sustainable NLP

Challenge:	Existing methods for estimating attention importance for tokens are ineffective . dLLMs require bidirectional attention, which limits inference efficiency .
Approach:	They propose a training-free attention sparsification framework for efficient long-context inference . they propose 'sink-aware pruning strategy' to accurately estimate and remove redundant computation .
Outcome:	The proposed approach offers 29 lossless speedup under 32K context length.

Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (2024.lrec-main)

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Yan Xiao, Yaochu Jin, Kuangrong Hao

Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023.acl-long)

Challenge:	Existing studies on relation extraction focus on document-level training without sharing raw medical texts.
Approach:	They propose a federated framework for relation extraction that enables collaborative training without sharing raw medical texts.
Outcome:	The proposed framework extends document-level relation extraction to a federated environment.

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Yuxin He, Jingyue Hu, Buzhou Tang

MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)

Challenge:	Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account.
Approach:	They propose to reformulate event co-occurrences as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework that extracts the arguments of multiple events in parallel.
Outcome:	The proposed framework can extract arguments of multiple events in parallel.

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Xuxin Cheng, Zhihong Zhu, Bowen Cao, Qichen Ye, Yuexian Zou

Semantic Parsing

Challenge:	Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions.
Approach:	They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference.
Outcome:	The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset.

Diffusion Language Model with Query-Document Relevance for Query-Focused Summarization (2023.findings-emnlp)

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Shaoyao Huang, Luozheng Qin, Ziqiang Cao

Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models (2023.findings-acl)

Challenge:	Query-Focused Summarization (QFS) aims to generate summaries that address specific queries by extracting crucial information from source documents.
Approach:	They propose a non-autoregressive diffusion language model that incorporates query-document fragment relevance and query-doctoral global relevance to enhance the adaptability of QFS tasks.
Outcome:	The proposed model achieves state-of-the-art performance on Debatepedia and PubMedQA datasets in ROUGE scores, GPT-4, and human evaluations.

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Hang Cao, Zhiquan Cao, Chi Hu, Baoyu Hou, Tong Xiao, Jingbo Zhu

Syntactic Text Processing

Challenge:	Autoregressive models assign low probabilities to tokens that need corrections . grammatical error correction (GEC) is widely applied to natural language processing tasks .
Approach:	They propose to use a non-autoregressive model as an auxiliary model to train GEC models to correct grammatical errors in sentences.
Outcome:	The proposed method outperforms baselines on English and Chinese GEC tasks significantly.

Maximal Clique Based Non-Autoregressive Open Information Extraction (2021.emnlp-main)

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Bowen Yu, Yucheng Wang, Tingwen Liu, Hongsong Zhu, Limin Sun, Bin Wang

Identifying Early Maladaptive Schemas from Mental Health Question Texts (2023.findings-emnlp)

Challenge:	Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence.
Approach:	They propose a non-autoregressive framework that generates a fact graph and a graph with an edge linking two nodes that belong to the same fact.
Outcome:	The proposed framework outperforms current state-of-the-art methods on two benchmark datasets and significantly outperformed the existing ones.

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Sujatha Gollapalli, Beng Ang, See-Kiong Ng

Responsible & Trustworthy NLP

Challenge:	Existing methods for identifying maladaptive schemas are not practical in 'offline' counseling scenarios such as community QA forums.
Approach:	They propose to use large language models and non-LLM methods to identify early maladaptive schemas (EMS) in Schema Therapy.
Outcome:	The proposed methods show complementary properties and can be used to further devise techniques for EMS identification.

Lost in Diffusion: Uncovering Hallucination Patterns and Failure Modes in Diffusion Large Language Models (2026.findings-acl)

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Zhengnan Guo, Fei Tan

Reasoning

Challenge:	dLLMs have emerged as a promising non-autoregressive paradigm for text generation, but their hallucination mechanisms remain underexplored.
Approach:	They present the first controlled comparative study to evaluate hallucination patterns in Diffusion Large Language Models.
Outcome:	The proposed model exhibits higher propensity for hallucination than AR counterparts controlled for architecture, scale, and pre-training weights.

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion (2025.findings-emnlp)

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Advait Joglekar, Divyanshu Singh, Rooshil Rohit Bhatia, Srinivasan Umesh

Low-Resource NLP

Challenge:	Current voice conversion methods struggle in zero-shot cross-lingual settings . authors develop a method that can be used in zero shot cross-linguistic settings despite advances in technology .
Approach:	They propose a voice-conversion model that combines discrete speech representations with a non-autoregressive speech decoder.
Outcome:	The proposed approach excels in zero-shot cross-lingual settings even for unseen languages and accents.

LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)

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Bingshen Mu, Xian Shi, Xiong Wang, Hexin Liu, Jin Xu, Lei Xie

Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs (2025.emnlp-main)

Challenge:	Existing methods for forcing alignment are language-specific and prone to temporal shifts.
Approach:	They propose a slot-filling paradigm that uses time indices to predict slot positions.
Outcome:	The proposed method reduces accumulated temporal shifts by 69% compared with prior methods.

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Daehoon Gwak, Minseo Jung, Junwoo Park, Minho Park, ChaeHun Park, Junha Hyung, Jaegul Choo