Decision Strategies for Incremental POS Tagging

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 26-33. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Adaptive Latency for Part-of-Speech Tagging in Incremental Text-to-Speech Synthesis

International audienceIncremental text-to-speech systems aim at synthesizing a text 'on-the-fly', while the user is typing a sentence. In this context, this article addresses the problem of the part-of-speech tagging (POS, i.e. lexical category) which is a critical step for accurate grapheme-to-phoneme conversion and prosody estimation. Here, the main challenge is to estimate the POS of a given word without knowing its 'right context' (i.e. the following words which are not available yet). To address this issue, we propose a method based on a set of decision trees estimating online whether a given POS tag is likely to be modified when more right-contextual information becomes available. In such a case, the synthesis is delayed until POS stability is guaranteed. This results in delivering the synthetic voice in word chunks of variable length. Objective evaluation on French shows that the proposed method is able to estimate POS tags with more than a 92% accuracy (compared to a non-incremental system) while minimizing the synthesis latency (between 1 and 4 words). Perceptual evaluation (ranking test) is then carried in the context of HMM-based speech synthesis. Experimental results show that the word grouping resulting from the proposed method is rated more acceptable than word-byword incremental synthesis

A hybrid architecture for robust parsing of german

This paper provides an overview of current research on a hybrid and robust parsing architecture for the morphological, syntactic and semantic annotation of German text corpora. The novel contribution of this research lies not in the individual parsing modules, each of which relies on state-of-the-art algorithms and techniques. Rather what is new about the present approach is the combination of these modules into a single architecture. This combination provides a means to significantly optimize the performance of each component, resulting in an increased accuracy of annotation

The Road to Quality is Paved with Good Revisions: A Detailed Evaluation Methodology for Revision Policies in Incremental Sequence Labelling

Incremental dialogue model components produce a sequence of output prefixes based on incoming input. Mistakes can occur due to local ambiguities or to wrong hypotheses, making the ability to revise past outputs a desirable property that can be governed by a policy. In this work, we formalise and characterise edits and revisions in incremental sequence labelling and propose metrics to evaluate revision policies. We then apply our methodology to profile the incremental behaviour of three Transformer-based encoders in various tasks, paving the road for better revision policies.Comment: Accepted at SIGdial 202

Named Entity Recognition in Twitter using Images and Text

Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.Comment: The 3rd International Workshop on Natural Language Processing for Informal Text (NLPIT 2017), 8 page

PARADISE: A Framework for Evaluating Spoken Dialogue Agents

This paper presents PARADISE (PARAdigm for DIalogue System Evaluation), a general framework for evaluating spoken dialogue agents. The framework decouples task requirements from an agent's dialogue behaviors, supports comparisons among dialogue strategies, enables the calculation of performance over subdialogues and whole dialogues, specifies the relative contribution of various factors to performance, and makes it possible to compare agents performing different tasks by normalizing for task complexity.Comment: 10 pages, uses aclap, psfig, lingmacros, time