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.
Our work extends the Recurrent Neural Network Language Model (RLM) of @cite_14 , which uses continuous representations to estimate a probability function over natural language sentences. We propose a set of conditional RLMs where contextual information (i.e., past utterances) is encoded in a continuous context vector to help generate the response. Our models differ from most previous work in the way the context vector is constructed. For example, and use a pre-trained topic model. In our models, the context vector is learned along with the conditional RLM that generates the response. Additionally, the learned context encodings do not exclusively capture contentful words. Indeed, even stop words'' can carry discriminative power in this task; for example, all words in the utterance how are you?'' are commonly characterized as stop words, yet this is a contentful dialog utterance.
@cite_14
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We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure. Neural language models (LMs) based on recurrent neural networks (RNN) are some of the most successful word and character-level LMs. Why do they work so well, in particular better than linear neural LMs? Possible explanations are that RNNs have an implicitly better regularization or that RNNs have a higher capacity for storing patterns due to their nonlinearities or both. Here we argue for the first explanation in the limit of little training data and the second explanation for large amounts of text data. We show state-of-the-art performance on the popular and small Penn dataset when RNN LMs are regularized with random dropout. Nonetheless, we show even better performance from a simplified, much less expressive linear RNN model without off-diagonal entries in the recurrent matrix. We call this model an impulse-response LM (IRLM). Using random dropout, column normalization and annealed learning rates, IRLMs develop neurons that keep a memory of up to 50 words in the past and achieve a perplexity of 102.5 on the Penn dataset. On two large datasets however, the same regularization methods are unsuccessful for both models and the RNN's expressivity allows it to overtake the IRLM by 10 and 20 percent perplexity, respectively. Despite the perplexity gap, IRLMs still outperform RNNs on the Microsoft Research Sentence Completion (MRSC) task. We develop a slightly modified IRLM that separates long-context units (LCUs) from short-context units and show that the LCUs alone achieve a state-of-the-art performance on the MRSC task of 60.8 . Our analysis indicates that a fruitful direction of research for neural LMs lies in developing more accessible internal representations, and suggests an optimization regime of very high momentum terms for effectively training such models. Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate. In this paper, we investigate and compare three different possibilities to convert recurrent neural network language models (RNNLMs) into backoff language models (BNLM). While RNNLMs often outperform traditional n-gram approaches in the task of language modeling, their computational demands make them unsuitable for an efficient usage during decoding in an LVCSR system. It is, therefore, of interest to convert them into BNLMs in order to integrate their information into the decoding process. This paper compares three different approaches: a text based conversion, a probability based conversion and an iterative conversion. The resulting language models are evaluated in terms of perplexity and mixed error rate in the context of the Code-Switching data corpus SEAME. Although the best results are obtained by combining the results of all three approaches, the text based conversion approach alone leads to significant improvements on the SEAME corpus as well while offering the highest computational efficiency. In total, the perplexity can be reduced by 11.4 relative on the evaluation set and the mixed error rate by 3.0 relative on the same data set.