Towards Understanding Egyptian Arabic Dialogues

Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0308

A Finite State and Data-Oriented Method for Grapheme to Phoneme Conversion

A finite-state method, based on leftmost longest-match replacement, is presented for segmenting words into graphemes, and for converting graphemes into phonemes. A small set of hand-crafted conversion rules for Dutch achieves a phoneme accuracy of over 93%. The accuracy of the system is further improved by using transformation-based learning. The phoneme accuracy of the best system (using a large set of rule templates and a `lazy' variant of Brill's algoritm), trained on only 40K words, reaches 99% accuracy.Comment: 8 page

Automatic extraction of rules for sentence boundary disambiguation

ABSTRACT Transformation-based learning (TBL) is the most important machine learning theory aiming at the automatic extraction of rules based on already tagged corpora. However, the application of this theory to a certain application without taking into account the features that characterize this application may cause problems regarding the training time cost as well as the accuracy of the extracted rules. In this paper we present a variation of the basic idea of the TBL and we apply it to the extraction of the sentence boundary disambiguation rules in real-world text, a prerequisite for the vast majority of the natural language processing applications. We show that our approach achieves considerably higher accuracy results and, moreover, requires minimal training time in comparison to the traditional TBL

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Dialogue Act Recognition Approaches

This paper deals with automatic dialogue act (DA) recognition. Dialogue acts are sentence-level units that represent states of a dialogue, such as questions, statements, hesitations, etc. The knowledge of dialogue act realizations in a discourse or dialogue is part of the speech understanding and dialogue analysis process. It is of great importance for many applications: dialogue systems, speech recognition, automatic machine translation, etc. The main goal of this paper is to study the existing works about DA recognition and to discuss their respective advantages and drawbacks. A major concern in the DA recognition domain is that, although a few DA annotation schemes seem now to emerge as standards, most of the time, these DA tag-sets have to be adapted to the specificities of a given application, which prevents the deployment of standardized DA databases and evaluation procedures. The focus of this review is put on the various kinds of information that can be used to recognize DAs, such as prosody, lexical, etc., and on the types of models proposed so far to capture this information. Combining these information sources tends to appear nowadays as a prerequisite to recognize DAs

Noise Robust Dialogue Act Recognition for Task-oriented Dialogues

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이상구.대화 시스템과 이메일, 게시글 요약 시스템 구축에 있어 대화 의도 분류는 중요한 역할을 한다. 이는 각각의 시스템들이 발화, 메일, 게시글 형태의 데이터에 대하여 대화 의도를 분류하고 이 정보를 하위 작업의 입력으로 사용하기 때문이다. 그래서 대화 의도 분류 성능이 해당 시스템 의 전체 성능에 크게 영향을 주기 때문에 성능 향상 측면에 있어 중요하다. 대화 의도 분류는 대화 내 발화에 대화 의도를 할당하는 문제이다. 특히 대화 시스템에서는 음성 인식 에러가 존재하기 때문에 에러에 강인한 대화 의도 분류 모델이 필요하다. 따라서 본 논문에서는 두 명의 사람이 특정 목적을 가지고 진행하는 과제 지향형 대화라는 상황에서 발화, 화자, 대화 의도를 고려하여 대화 구조를 모사하는 생성모델을 만들어 노이즈 데이터에 대응하였다. 이 모델의 기반이 되는 가정은 화자는 어떠한 행위를 수행하고자 하는 목적을 가지고, 그 목적에 맞는 적절한 어휘 집합을 사용하여 상대방에게 말을 한다는 것이다. 즉 제안한 모델은 이러한 가정을 고려하여 마르코프 모델을 개선하였다. 과제 지향형 데이터인 HCRC map task, live chat, SACTI-1 말뭉치를 이용한 실험을 통해 제안한 모델이 기존 마르코프 모델에 비하여 더 나은 성능을 보이고, 현재까지도 대화 의도 분류 성능이 높은 SVM-HMM과 경쟁력 있는 결과를 보이는 것을 확인 하였다. 특히 대화 시스템의 음성 인식 모듈의 에러를 모방한 SACTI-1 말뭉치에 대하여 제안한 모델이 SVM-HMM에 비하여 노이즈에 강인함을 보였다.In spoken dialog system, e-mail summary system and thread summary system development, dialogue act classifier plays an important role because the systems depend on the performance of classifying dialogue acts of utterances, e-mails and posts to improve completeness of the system. The dialogue act classification problem is a well-known problem to assign the dialogue acts to utterances in a conversation. One of the main challenges in the development of robust dialog systems is especially to deal with noisy input due to imperfect results from Automatic Speech Recognition (ASR) module. The challenge in dialogue act recognition is the mapping from noisy user utterances to dialogue acts. In this paper, to cope with noisy utterances, we describe a noise robust generative model of task-oriented conversation that captures both the speaker information and the dialogue act associated with each utterance under the assumption that a speaker says about something by using appropriate vocabulary with the aim of getting someone to do somethings. The proposed model is based on Markov model and is modified to reflect the assumption. In the experiments, we evaluate the classification results by comparing them to the simple Markov model and state-of-the-art SVM-HMM results. The proposed model is a better conversation model than the simple Markov model and shows the competitive classification results in comparison with SVM-HMM in the task-oriented HCRC map task corpus, live-chat corpus and SACTI-1 corpus. Results based on SACTI-1 corpus which simulates ASR errors particularly show that the proposed model is robust against noisy user utterances.1. 서론 1 1.1 연구의 배경 1 1.2 연구의 내용 및 범위 3 1.3 논문의 구성 6 2. 문제 정의 7 2.1 대화문의 구성요소 7 2.2 대화 의도 분류 문제 정의 12 2.3 대화문의 특징 및 문제 해결의 어려운 점 13 3. 관련 연구 15 3.1 지도 학습 기반의 대화 의도 분류 연구 15 3.2 대화 의도의 의존 관계를 모델링 한 연구 16 3.3 기존 연구의 한계점 22 4. 마르코프 모델 기반 대화 의도 분류 24 4.1 배경지식 24 4.1.1 언어모델 24 4.1.2 마르코프 모델과 은닉 마르코프 모델 25 4.2 입출력 마르코프 모델을 변형한 대화 의도 분류 모델 26 5. 성능 평가 31 5.1 대화 말뭉치 31 5.2 비교모델 및 개발환경 38 5.3 성능 평가 측정치 39 5.4 실험 결과 및 분석 40 5.4.1 분류 성능 41 5.4.2 ASR 노이즈에 대한 강인성 45 5.4.3 확장성 48 6. 결론 및 향후 연구 50 6.1 결론 50 6.2 향후 연구 51 참고문헌 53 ABSTRACT 57Maste