Encoder-decoder or sequence-to-sequence models are used for a different kind of sequence modeling in which the output sequence is a complex function of the entire input sequencer; we must map from a sequence of input words or tokens to a sequence of tags that are not merely direct mappings from individual words.

greater flexibility across a range of applications. Specifically, we’ll introduce encoder-decoder networks, or sequence-to-sequence models, that are capable of generating contextually appropriate, arbitrary length, output sequences. Encoder-decoder net-works have been applied to a very wide range of applications including machine

What is Encoder –Decoder Model? Encoder - Decoder model is a Machine Learning model comprising of two learning components (two neural networks in this context) called Encoder and Decoder. The first network works normally, and the second network works in reverse manner

Neural model with a sequence of discrete symbols as an input that generates another sequence of discrete symbols as an output. What is it good for? Neural decoder is a conditional language model. In every step some information goes in and some information goes out. Image on the right: Chris Olah. Understanding LSTM Networks.

Specifically, we’ll introduce encoder‐decoder networks, or sequence‐to‐sequence models, that are capable of generating contextually appropriate, arbitrary length, output sequences.

In this paper, we explore how encoder-decoder models can be used effectively for extracting relation tuples from sentences. There are three major challenges in this task: (i) The model should be able to extract entities and relations to- gether. (ii) It should be able to extract multiple tuples with overlapping entities.

Acoustic model (encoder) and language model (prediction network) parts are modelled independently and combined in the joint network. However everything is optimised to a common sequence-level objective (using the CTC loss function). With su cient training data, additional language and pronunciation models are not necessary (Google experiments)

the encoder-decoder based models to solve sophisticated tasks such as image/video captioning, textual/visual question answering, and text summarization. In this work we study the baseline encoder-decoder framework in machine translation and take a brief look at the encoder structures proposed to cope with the difficulties of fea-ture extraction.

Figure 8.17 Translating a single sentence (inference time) in the basic RNN version of encoder-decoder ap-proach to machine translation. Source and target sentences are concatenated with a separator token in between, and the decoder uses context …

In this work, we investigate how encoder-decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components.