Add to ORKGInternational audienceWe present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known models such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model. We evaluate the toolkit on two datasets and we show that the toolkit performs similarly or even better than a very widely used sequence-to-sequence toolkit
Session 6A: Machine learningWe address an important problem in sequence-to-sequence (Seq2Seq) learni...
Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data proble...
Neural sequence model, though widely used for modeling sequential data such as the language model, h...
In Natural Language Processing (NLP), it is important to detect the relationship between two sequenc...
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example i...
We develop a precise writing survey on sequence-to-sequence learning with neural network and its mod...
Chatbot for education has great potential to complement human educators and education administrators...
Chatbot for education has great potential to complement human educators and education administrators...
Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper i...
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequen...
Abstract Sequence-to-sequence models have achieved impressive results on various tasks. However, the...
This paper presents the SEQ model which is the basis for a system to manage various kinds of sequenc...
Sequence processing involves several tasks such as clustering, classification, prediction, and trans...
Conditional set generation learns a mapping from an input sequence of tokens to a set. Several NLP t...
In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a numbe...
Session 6A: Machine learningWe address an important problem in sequence-to-sequence (Seq2Seq) learni...
Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data proble...
Neural sequence model, though widely used for modeling sequential data such as the language model, h...
In Natural Language Processing (NLP), it is important to detect the relationship between two sequenc...
Neural sequence-to-sequence models are finding increasing use in editing of documents, for example i...
We develop a precise writing survey on sequence-to-sequence learning with neural network and its mod...
Chatbot for education has great potential to complement human educators and education administrators...
Chatbot for education has great potential to complement human educators and education administrators...
Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper i...
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequen...
Abstract Sequence-to-sequence models have achieved impressive results on various tasks. However, the...
This paper presents the SEQ model which is the basis for a system to manage various kinds of sequenc...
Sequence processing involves several tasks such as clustering, classification, prediction, and trans...
Conditional set generation learns a mapping from an input sequence of tokens to a set. Several NLP t...
In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a numbe...
Session 6A: Machine learningWe address an important problem in sequence-to-sequence (Seq2Seq) learni...
Traditional machine learning sequence models, such as RNN and LSTM, can solve sequential data proble...
Neural sequence model, though widely used for modeling sequential data such as the language model, h...