3 research outputs found
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
Reconocimiento de acto de diálogo secuencial para debates argumentativos árabes
Dialogue act recognition remains a primordial task that helps user to automatically identify participants’ intentions. In this paper, we propose a sequential approach consisting of segmentation followed by annotation process to identify dialogue acts within Arabic politic debates. To perform DA recognition, we used the CARD corpus labeled using the SADA annotation schema. Segmentation and annotation tasks were then carried out using Conditional Random Fields probabilistic models as they prove high performance in segmenting and labeling sequential data. Learning results are notably important for the segmentation task (F-score=97.9%) and relatively reliable within the annotation process (f-score=63.4%) given the complexity of identifying argumentative tags and the presence of disfluencies in spoken conversations.El reconocimiento del acto de diálogo sigue siendo una tarea primordial que ayuda al usuario a identificar automáticamente las intenciones de los participantes. En este documento, proponemos un enfoque secuencial que consiste en la segmentación seguida de un proceso de anotación para identificar actos de diálogo dentro de los debates polÃticos árabes. Para realizar el reconocimiento DA, utilizamos el corpus CARD etiquetado utilizando el esquema de anotación SADA. Las tareas de segmentación y anotación se llevaron a cabo utilizando modelos probabilÃsticos de Campos aleatorios condicionales, ya que demuestran un alto rendimiento en la segmentación y el etiquetado de datos secuenciales. Los resultados de aprendizaje son especialmente importantes para la tarea de segmentación (F-score = 97.9%) y relativamente confiables dentro del proceso de anotación (f-score = 63.4%) dada la complejidad de identificar etiquetas argumentativas y la presencia de disfluencias en las conversaciones habladas