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1. Grounded Sequence to Sequence Transduction

   https://doi.org/10.1109/JSTSP.2020.2998415  

   Lucia Specia, Loıc Barrault ( July 2020 , IEEE journal of selected topics in signal processing)

   null (Ed.)

   Speech recognition and machine translation have made major progress over the past decades, providing practical systems to map one language sequence to another. Although multiple modalities such as sound and video are becoming increasingly available, the state-of-the-art systems are inherently unimodal, in the sense that they take a single modality --- either speech or text --- as input. Evidence from human learning suggests that additional modalities can provide disambiguating signals crucial for many language tasks. Here, we describe the dataset, a large, open-domain collection of videos with transcriptions and their translations. We then show how this single dataset can be used to develop systems for a variety of language tasks and present a number of models meant as starting points. Across tasks, we find that building multi-modal architectures that perform better than their unimodal counterpart remains a challenge. This leaves plenty of room for the exploration of more advanced solutions that fully exploit the multi-modal nature of the dataset, and the general direction of multimodal learning with other datasets as well. 

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2. Deep Reinforcement Learning for Sequence-to-Sequence Models

   https://doi.org/10.1109/TNNLS.2019.2929141  

   Keneshloo, Yaser ; Shi, Tian ; Ramakrishnan, Naren ; Reddy, Chandan K. ( July 2020 , IEEE Transactions on Neural Networks and Learning Systems)

   In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity and provide stateof-the-art performance in a wide variety of tasks, such as machine translation, headline generation, text summarization, speech-to-text conversion, and image caption generation. The underlying framework for all these models is usually a deep neural network comprising an encoder and a decoder. Although simple encoder–decoder models produce competitive results, many researchers have proposed additional improvements over these seq2seq models, e.g., using an attention-based model over the input, pointer-generation models, and self-attention models. However, such seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently, a completely novel point of view has emerged in addressing these two problems in seq2seq models, leveraging methods from reinforcement learning (RL). In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with seq2seq models that enable remembering long-term memories. We present some of the most recent frameworks that combine the concepts from RL and deep neural networks. Our work aims to provide insights into some of the problems that inherently arise with current approaches and how we can address them with better RL models. We also provide the source code for implementing most of the RL models discussed in this paper to support the complex task of abstractive text summarization and provide some targeted experiments for these RL models, both in terms of performance and training time. 

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3. Sequence-to-sequence AMR Parsing with Ancestor Information

   https://doi.org/10.18653/v1/2022.acl-short.63  

   Yu, Chen ; Gildea, Daniel ( January 2022 , Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics)
4. Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

   https://doi.org/10.1021/acscentsci.7b00303  

   Liu, Bowen ; Ramsundar, Bharath ; Kawthekar, Prasad ; Shi, Jade ; Gomes, Joseph ; Luu Nguyen, Quang ; Ho, Stephen ; Sloane, Jack ; Wender, Paul ; Pande, Vijay ( October 2017 , ACS Central Science)
5. Learning Stress Patterns with a Sequence-to-Sequence Neural Network

   https://doi.org/https://doi.org/10.7275/xdbc-6925  

   Prickett, Brandon ; Pater, Joe ( January 2022 , Proceedings of the Society for Computation in Linguistics)

   Ettinger, Allyson ; Hunter, Tim ; Prickett, Brandon (Ed.)

   We present the first application of modern neural networks to the well studied task of learning word stress systems. We tested our adaptation of a sequence-to-sequence network on the Tesar and Smolensky test set of 124 “languages”, showing that it acquires generalizable representations of stress patterns in a very high proportion of runs. We also show that it learns restricted lexically conditioned patterns, known as stress windows. The ability of this model to acquire lexical idiosyncracies, which are very common in natural language systems, sets it apart from past, non-neural models tested on the Tesar and Smolensky data set. 

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