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Sequence-to-Sequence

42 papers

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Papers

SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding ACL 2026 Acquiring Pronunciation from Speech Audio via Multi-task Learning Interspeech 2025 A Perception-Based L2 Speech Intelligibility Indicator: Leveraging a Rater’s Shadowing and Sequence-to-sequence Voice Conversion Interspeech 2025 Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge Interspeech 2025 Neural Text Normalization for Luxembourgish Using Real-Life Variation Data COLING 2025 PreP-OCR: A Complete Pipeline for Document Image Restoration and Enhanced OCR Accuracy ACL 2025 BAYES RISK CTC: CONTROLLABLE CTC ALIGNMENT IN SEQUENCE-TO-SEQUENCE TASKS ICLR 2023 DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models ICLR 2023 Leveraging Denoised Abstract Meaning Representation for Grammatical Error Correction ACL 2023 Multiple sequence alignment as a sequence-to-sequence learning problem ICLR 2023 Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks ICLR 2023 Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees ICLR 2023 TypeT5: Seq2seq Type Inference using Static Analysis ICLR 2023 Unsupervised Open-domain Keyphrase Generation ACL 2023 On the approximation properties of recurrent encoder-decoder architectures ICLR 2022 SketchODE: Learning neural sketch representation in continuous time ICLR 2022 Autoregressive Entity Retrieval ICLR 2021 Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation ICLR 2021 FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation EMNLP 2021 Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems EMNLP 2021 Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning ICLR 2021 Compositional Language Continual Learning ICLR 2020 Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog EMNLP 2020 What Have We Achieved on Text Summarization? EMNLP 2020 Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models ICLR 2019