Clustering of syntactic and discursive information for the dynamic adaptation of Language Models

Presentamos una estrategia de agrupamiento de elementos de diálogo, de tipo semántico y discursivo. Empleando Latent Semantic Analysis (LSA) agru- pamos los diferentes elementos de acuerdo a un criterio de distancia basado en correlación. Tras seleccionar un conjunto de grupos que forman una partición del espacio semántico o discursivo considerado, entrenamos unos modelos de lenguaje estocásticos (LM) asociados a cada modelo. Dichos modelos se emplearán en la adaptación dinámica del modelo de lenguaje empleado por el reconocedor de habla incluido en un sistema de diálogo. Mediante el empleo de información de diálogo (las probabilidades a posteriori que el gestor de diálogo asigna a cada elemento de diálogo en cada turno), estimamos los pesos de interpolación correspondientes a cada LM. Los experimentos iniciales muestran una reducción de la tasa de error de palabra al emplear la información obtenida a partir de una frase para reestimar la misma frase

Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

Improving Variational Encoder-Decoders in Dialogue Generation

Variational encoder-decoders (VEDs) have shown promising results in dialogue generation. However, the latent variable distributions are usually approximated by a much simpler model than the powerful RNN structure used for encoding and decoding, yielding the KL-vanishing problem and inconsistent training objective. In this paper, we separate the training step into two phases: The first phase learns to autoencode discrete texts into continuous embeddings, from which the second phase learns to generalize latent representations by reconstructing the encoded embedding. In this case, latent variables are sampled by transforming Gaussian noise through multi-layer perceptrons and are trained with a separate VED model, which has the potential of realizing a much more flexible distribution. We compare our model with current popular models and the experiment demonstrates substantial improvement in both metric-based and human evaluations.Comment: Accepted by AAAI201