You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Ever since the successful application of sequence to sequence learning for neural machine translation systems, interest has surged in its applicability towards language generation in other problem domains. Recent work has investigated the use of these neural architectures towards modeling open-domain conversational dialogue, where it has been found that although these models are capable of learning a good distributional language model, dialogue coherence is still of concern. Unlike translation, conversation is much more a one-to-many mapping from utterance to a response, and it is even more pressing that the model be aware of the preceding flow of conversation. In this paper we propose to tackle this problem by introducing previous conversational context in terms of latent representations of dialogue acts over time. We inject the latent context representations into a sequence to sequence neural network in the form of dialog acts using a second encoder to enhance the quality and the coherence of the conversations generated. The main task of this research work is to show that adding latent variables that capture discourse relations does indeed result in more coherent responses when compared to conventional sequence to sequence models.

Cited Abstract(s):
[1]: Documents exhibit sequential structure at multiple levels of abstraction (e.g., sentences, paragraphs, sections). These abstractions constitute a natural hierarchy for representing the context in which to infer the meaning of words and larger fragments of text. In this paper, we present CLSTM (Contextual LSTM), an extension of the recurrent neural network LSTM (Long-Short Term Memory) model, where we incorporate contextual features (e.g., topics) into the model. We evaluate CLSTM on three specific NLP tasks: word prediction, next sentence selection, and sentence topic prediction. Results from experiments run on two corpora, English documents in Wikipedia and a subset of articles from a recent snapshot of English Google News, indicate that using both words and topics as features improves performance of the CLSTM models over baseline LSTM models for these tasks. For example on the next sentence selection task, we get relative accuracy improvements of 21 for the Wikipedia dataset and 18 for the Google News dataset. This clearly demonstrates the significant benefit of using context appropriately in natural language (NL) tasks. This has implications for a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems.

[2]: We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.
With all this said, there has been very little focus on incorporating the context of the conversation that has happened so far in order to generate responses. [2] propose a naive approach where they incorporate the previous set of utterance, responses as a bag of words model and use a feed forward neural network to inject a fixed sized context vector as a into the LSTM cell of the encoder. [1] proposed a modified LSTM cell with an additional gate that incorporates the previous context as input during encoding. The weights of the gate are learned exactly in the same way the weights for the input, forget and output gates are learned.