{"list":"other","total":56,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1508.07909","title":"Neural Machine Translation of Rare Words with Subword Units","created":"2015-08-31","cited_by_count":2918},{"id":"2005.08100","title":"Conformer: Convolution-augmented Transformer for Speech Recognition","created":"2020-05-16","cited_by_count":1863},{"id":"1804.08771","title":"A Call for Clarity in Reporting BLEU Scores","created":"2018-04-23","cited_by_count":1799},{"id":"1904.08779","title":"SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition","created":"2019-04-18","cited_by_count":1380},{"id":"1906.04341","title":"What Does BERT Look At? An Analysis of BERT's Attention","created":"2019-06-11","cited_by_count":1003},{"id":"1606.07947","title":"Sequence-Level Knowledge Distillation","created":"2016-06-25","cited_by_count":801},{"id":"1308.0850","title":"Generating Sequences With Recurrent Neural Networks","created":"2013-08-04","cited_by_count":554},{"id":"1508.01211","title":"Listen, Attend and Spell","created":"2015-08-05","cited_by_count":543},{"id":"1908.08593","title":"Revealing the Dark Secrets of BERT","created":"2019-08-21","cited_by_count":301},{"id":"1906.04284","title":"Analyzing the Structure of Attention in a Transformer Language Model","created":"2019-06-07","cited_by_count":243},{"id":"2010.05171","title":"fairseq S2T: Fast Speech-to-Text Modeling with fairseq","created":"2020-10-11","cited_by_count":176},{"id":"2004.08728","title":"SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings","created":"2020-04-18","cited_by_count":156},{"id":"1508.04395","title":"End-to-End Attention-based Large Vocabulary Speech Recognition","created":"2015-08-18","cited_by_count":151},{"id":"2010.13956","title":"Recent Developments on ESPnet Toolkit Boosted by Conformer","created":"2020-10-26","cited_by_count":132},{"id":"1703.08581","title":"Sequence-to-Sequence Models Can Directly Translate Foreign Speech","created":"2017-03-24","cited_by_count":121},{"id":"1612.01744","title":"Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation","created":"2016-12-06","cited_by_count":113},{"id":"1906.05218","title":"Monotonic Infinite Lookback Attention for Simultaneous Machine Translation","created":"2019-06-12","cited_by_count":104},{"id":"2007.16193","title":"SimulEval: An Evaluation Toolkit for Simultaneous Translation","created":"2020-07-31","cited_by_count":94},{"id":"1909.12406","title":"Monotonic Multihead Attention","created":"2019-09-26","cited_by_count":93},{"id":"1911.12246","title":"Do Attention Heads in BERT Track Syntactic Dependencies?","created":"2019-11-27","cited_by_count":88},{"id":"1704.00784","title":"Online and Linear-Time Attention by Enforcing Monotonic Alignments","created":"2017-04-03","cited_by_count":85},{"id":"2011.02048","title":"SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation","created":"2020-11-03","cited_by_count":81},{"id":"1909.02074","title":"Jointly Learning to Align and Translate with Transformer Models","created":"2019-09-04","cited_by_count":75},{"id":"2206.05807","title":"Over-Generation Cannot Be Rewarded: Length-Adaptive Average Lagging for Simultaneous Speech Translation","created":"2022-06-12","cited_by_count":57},{"id":"2206.00888","title":"Squeezeformer: An Efficient Transformer for Automatic Speech Recognition","created":"2022-06-02","cited_by_count":51},{"id":"2005.11185","title":"Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection","created":"2020-05-22","cited_by_count":49},{"id":"2102.01578","title":"CTC-based Compression for Direct Speech Translation","created":"2021-02-02","cited_by_count":45},{"id":"2205.11631","title":"Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer","created":"2022-05-23","cited_by_count":45},{"id":"2004.13169","title":"Simultaneous Translation Policies: From Fixed to Adaptive","created":"2020-04-27","cited_by_count":43},{"id":"2109.01163","title":"Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition","created":"2021-08-31","cited_by_count":42},{"id":"2106.04833","title":"RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer","created":"2021-06-09","cited_by_count":40},{"id":"2210.12357","title":"Information-Transport-based Policy for Simultaneous Translation","created":"2022-10-22","cited_by_count":40},{"id":"2004.14837","title":"Accurate Word Alignment Induction from Neural Machine Translation","created":"2020-04-30","cited_by_count":32},{"id":"2106.06636","title":"Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR","created":"2021-06-11","cited_by_count":31},{"id":"1901.11359","title":"Adding Interpretable Attention to Neural Translation Models Improves Word Alignment","created":"2019-01-31","cited_by_count":30},{"id":"2011.00033","title":"Streaming Simultaneous Speech Translation with Augmented Memory Transformer","created":"2020-10-30","cited_by_count":30},{"id":"2204.06028","title":"CUNI-KIT System for Simultaneous Speech Translation Task at IWSLT 2022","created":"2022-04-12","cited_by_count":30},{"id":"1906.00048","title":"Thinking Slow about Latency Evaluation for Simultaneous Machine Translation","created":"2019-05-31","cited_by_count":28},{"id":"2204.03783","title":"Does Simultaneous Speech Translation need Simultaneous Models?","created":"2022-04-08","cited_by_count":22},{"id":"1810.07595","title":"An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation","created":"2018-10-17","cited_by_count":15},{"id":"2109.04574","title":"Speechformer: Reducing Information Loss in Direct Speech Translation","created":"2021-09-09","cited_by_count":15},{"id":"2103.03233","title":"An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies","created":"2021-03-04","cited_by_count":14},{"id":"2204.09595","title":"Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech Translation","created":"2022-03-22","cited_by_count":14},{"id":"2205.02629","title":"Efficient yet Competitive Speech Translation: FBK@IWSLT2022","created":"2022-05-05","cited_by_count":14},{"id":"2107.00279","title":"The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021","created":"2021-07-01","cited_by_count":13},{"id":"2009.04707","title":"On Target Segmentation for Direct Speech Translation","created":"2020-09-10","cited_by_count":12},{"id":"1607.01628","title":"Guided Alignment Training for Topic-Aware Neural Machine Translation","created":"2016-07-06","cited_by_count":11},{"id":"2012.04964","title":"On Knowledge Distillation for Direct Speech Translation","created":"2020-12-09","cited_by_count":11},{"id":"2107.08807","title":"Simultaneous Speech Translation for Live Subtitling: from Delay to Display","created":"2021-07-19","cited_by_count":8},{"id":"2110.15729","title":"Decision Attentive Regularization to Improve Simultaneous Speech Translation Systems","created":"2021-10-13","cited_by_count":6},{"id":"2106.10840","title":"Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling","created":"2021-06-21","cited_by_count":5},{"id":"2208.07657","title":"Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition","created":"2022-08-16","cited_by_count":5},{"id":"2211.05967","title":"Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation","created":"2022-11-11","cited_by_count":4}],"next_cursor":null}