An Efficient Implementation of the Head-Corner Parser

This paper describes an efficient and robust implementation of a bi-directional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a review of the motivation for head-driven parsing strategies, and head-corner parsing in particular, a non-deterministic version of the head-corner parser is presented. A memoization technique is applied to obtain a fast parser. A goal-weakening technique is introduced which greatly improves average case efficiency, both in terms of speed and space requirements. I argue in favor of such a memoization strategy with goal-weakening in comparison with ordinary chart-parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser, while no practical loss in time-efficiency is observed. On the contrary, experiments are described in which head-corner and left-corner parsers implemented with selective memoization and goal weakening outperform `standard' chart parsers. The experiments include the grammar of the OVIS system and the Alvey NL Tools grammar. Head-corner parsing is a mix of bottom-up and top-down processing. Certain approaches towards robust parsing require purely bottom-up processing. Therefore, it seems that head-corner parsing is unsuitable for such robust parsing techniques. However, it is shown how underspecification (which arises very naturally in a logic programming environment) can be used in the head-corner parser to allow such robust parsing techniques. A particular robust parsing model is described which is implemented in OVIS.Comment: 31 pages, uses cl.st

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

Muslim Women’s Views on Dress Code and the Hijaab: Some Issues for Education

Recent French and Turkish bans on Muslim women wearing Islamic head coverings in schools,colleges and universities starts this discussion of religious discrimination and the necessity of interreligious open dialogue in which neither side holds entrenched positions. The paper examines ethnographically and explains the varied attitudes of Muslim women for and against their traditional dress code. It locates this issue in the critical educational concern for equity and argues for open and honest dialogue to help the next generation tackle global insecurities

Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer

With the increasing research interest in dialogue response generation, there is an emerging branch formulating this task as selecting next sentences, where given the partial dialogue contexts, the goal is to determine the most probable next sentence. Following the recent success of the Transformer model, this paper proposes (1) a new variant of attention mechanism based on multi-head attention, called highway attention, and (2) a recurrent model based on transformer and the proposed highway attention, so-called Highway Recurrent Transformer. Experiments on the response selection task in the seventh Dialog System Technology Challenge (DSTC7) show the capability of the proposed model of modeling both utterance-level and dialogue-level information; the effectiveness of each module is further analyzed as well

Reflections from Participants

The Road Ahead: Public Dialogue on Science and Technology brings together some of the UK’s leading thinkers and practitioners in science and society to ask where we have got to, how we have got here, why we are doing what we are doing and what we should do next. The collection of essays aims to provide policy makers and dialogue deliverers with insights into how dialogue could be used in the future to strengthen the links between science and society. It is introduced by Professor Kathy Sykes, one of the UK’s best known science communicators, who is also the head of the Sciencewise-ERC Steering Group, and Jack Stilgoe, a DEMOS associate, who compiled the collection

In Singapore, the Shangri-La Dialogue reflected tensions in Asian security

2002 was a year of crisis between Pakistan and India, which almost resulted in war.This was also the year of the first Asia Security Summit, convened in Singapore by the London head-quartered International Institute for Strategic Studies (IISS): the Shangri-La Dialogue. The IISS chief executive Dr John Chipman informed the audience in that year’s Dialogue about the connection between the Indo-Pakistani conflict and the summit’s mitigating role, showing that summit diplomacy can mean more than simply words to be forgotten. Apart from the role of the Shangri-La Dialogue in appeasing the Indo-Pakistani conflict, another example he quoted was of the IISS’s Middle East Manama Dialogue 2013, where Chuck Hagel promised to foster links with the Gulf Cooperation Council countries, and followed up on that task in 2014

Predicting Head Pose in Dyadic Conversation

Natural movement plays a significant role in realistic speech animation. Numerous studies have demonstrated the contribution visual cues make to the degree we, as human observers, find an animation acceptable. Rigid head motion is one visual mode that universally co-occurs with speech, and so it is a reasonable strategy to seek features from the speech mode to predict the head pose. Several previous authors have shown that prediction is possible, but experiments are typically confined to rigidly produced dialogue. Expressive, emotive and prosodic speech exhibit motion patterns that are far more difficult to predict with considerable variation in expected head pose. People involved in dyadic conversation adapt speech and head motion in response to the others’ speech and head motion. Using Deep Bi-Directional Long Short Term Memory (BLSTM) neural networks, we demonstrate that it is possible to predict not just the head motion of the speaker, but also the head motion of the listener from the speech signal

What linguists always wanted to know about german and did not know how to estimate

This paper profiles significant differences in syntactic distribution and differences in word class frequencies for two treebanks of spoken and written German: the TüBa-D/S, a treebank of transliterated spontaneous dialogues, and the TüBa-D/Z treebank of newspaper articles published in the German daily newspaper die tageszeitung´(taz). The approach can be used more generally as a means of distinguishing and classifying language corpora of different genres