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Notes/Discussions on T-ASL EDICS Revision. Summary: In Language: From SLP to HLT, and adding Machine Learning for Language processing In Speech: Adding “Deep Learning” In Audio: Adding Music IR and “Semantics” in Audio SP Details in the following slides (starting with “Language”).
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Notes/Discussions on T-ASL EDICS Revision Summary: In Language: From SLP to HLT, and adding Machine Learning for Language processing In Speech: Adding “Deep Learning” In Audio: Adding Music IR and “Semantics” in Audio SP Details in the following slides (starting with “Language”)
The Old SLP EDICS • SLP-UNDE - Spoken Language Understanding Paralinguistic (emotion, age, gender, rate, etc.) information; nonlinguistic (meaning external to language) information, gestures, etc.; semantic classification; question/answering from speech; entity extraction from speech; spoken document summarization; detecting linguistic/discourse structure (e.g., disfluencies, sentence/topic boundaries, speech acts); relation to and interpretation of sign language. • SLP-LADL - Human Language Acquisition, Development and Learning Language acquisition, development, and learning models; computer aids for language learning; attributes and modeling techniques for assessment of language fluency. • SLP-SMMD - Spoken and Multimodal Dialog Systems Spoken and multimodal dialog systems, applications, and architectures; stochastic Learning for dialog modeling; response Generation; technologies for the aged; evaluations and standardizations; speech/voice-based human-computer interfaces (HCI); speech HCI for individuals with impairments and universal access (UA); other applications. • SLP-SMIR - Speech Data Mining and Document RetrievalAnalysis and Evaluations for mining spoken data; search/retrieval of speech documents; mining heterogeneous speech and multimedia data; speech data mining theory, algorithms, and methods; core machine learning algorithms for data mining; topic spotting and classification; pattern discovery and prediction from data; applications and tools for speech data mining. • SLP-SSMT - Machine Translation of SpeechSemi-automatic and data driven methods; speech processing for MTS; corpora, annotation, and other resources; interlingua and transfer approaches; integration of speech and linguistic processing; machine transliteration for named entity; evaluation metrics (e.g., BLEU); systems and applications for MTS. • SLP-LANG - Language Modeling (for Speech and SLP)N-grams, their generalizations and smoothing methods; language model adaptation; grammar based language modeling; maxent and feature based language modeling; dialect, accent, and idiolect at the language level; discriminative LM training methods; other approaches to LMs; structured classification approaches. • SLP-REAN - Spoken Language Resources and AnnotationGeneral corpora, annotation, and other resource
The Task Objectives: • To facilitate possible merger of IEEE T-ASL and ACM T-SLP • To cover both ‘spoken language processing’ and selected topics in ‘natural language processing (computational linguistics) with a focus on ‘processing’ and ‘computational’ for linguistic topics Considerations • To cover mainly both EDICS of IEEE T-ASL and ACM T-SLP • To reflect emerging new areas, increasing interests HLT from IEEE community • To use the technical areas of established journals and conferences as a reference Contributors • Haizhou Li, IEEE T-ASLP AE, ACM T-SLP AE • Li Deng, EiC, IEEE T-ASLP • Pascale Fung, IEEE T-ASLP AE, ACM T-SLP AE, TACL AE • Dilek Hakkani-Tur, IEEE T-ASLP AE • Jian Su, ACL Executive Board Member; TACL AE • Gokhan Tur, IEEE T-ASLP AE EDICS Review • December 2012 – June 2013
Summary of Changes • From Spoken Language Processing to Human Language Processing • Increase 7 subsections to 9 subsections • Re-organize 9 subsections as follows • HLT-LANG (Language Modeling, add computational phonology and phonetics) • HLT-MTSW (Machine Translation for Spoken and Written Language, add ‘text’ translation topics) • HLT-UNDE (Spoken Language Understanding and Computational Semantics) • HLT-DIAL (Discourse and Dialog) • HLT- SDTM (Spoken Document Retrieval and Text Mining, add NLP topic related to text mining and IR) • HLT-STPA (Segmentation, Tagging, and Parsing, new topic to cover core sentence-level language processing topics - word segmentation, tagging and parsing) • HLT- HLLI (Human Language Learning and Interface) • HLT-MLMD (Machine Learning Methods, new topic to reflect increasing interests) • HLT-LRSE (Language Resources and System Evaluation)
The HLT New Section • HLT-LANG (Language Modelling)N-grams, their generalizations and smoothing methods; language model adaptation: grammar-based, structured language modelling; discriminative, maximum-entropy and feature-based language modelling; computational phonology and phonetics; dialect, accent, and idiolect at the language level; • HLT-MTSW (Machine Translation for Spoken and Written Language)Example/phrase/syntax/semantics-based machine translation; hybrid machine translation: word/sentence/document alignments; synchronous grammar induction; decoding; system combination; post-editing; machine transliteration and transcription; spoken language translation: speech processing for machine translation; • HLT-UNDE (Spoken Language Understanding and Computational Semantics )Spoken language understanding; paralinguistic (emotion , age, gender, etc.), non-linguistic (gesture, sign, etc) Information processing; semantic role labelling, multiword expressions; word sense disambiguation, representation of meaning; lexical semantics; distributional semantics; text entailment; ontology; • HLT-DIAL (Discourse and Dialog)Learning of linguistic/discourse structure (e.g., disfluencies, sentence/topic boundaries, speech acts); co-reference and anaphora resolution; dialog management/generation/analysis; semantic analysis for discourse and dialog: intent determination: dialog act tagging; • HLT-SDTM (Spoken Document Retrieval and Text Mining)Spoken document retrieval; linguistic pattern discovery and prediction from data; spoken term detection: named entity recognition; question answering; document summarization and generation; spoken document summarization; information extraction and retrieval; subjectivity and sentiment analysis; text and spoken document classification; spam detection; topic detection and tracking; trend detection; • HLT-STPA (Segmentation, Tagging, and Parsing)Morphology analysis; word segmentation; part-of-speech tagging, chunking and supertagging; models and algorithms for parsing; grammar induction; dependency parsing; multilingual parsing; • HLT-HLLI (Human Language Learning and Interface )Language acquisition, development, and learning models; computer aids for language learning; assessment of language fluency; human computer interface; assistive technology for the aged, universal access and individuals with Impairments; • HLT-MLMD (Machine Learning Methods )Supervised, unsupervised, semi-supervised learning; statistical methods; symbolic learning methods; biologically inspired and neural networks; reinforcement learning; active learning; online learning; deep learning; recurs1ve and structured models, graphical and latent variable models; kernel methods; domain adaptation; • HLT-LRSE (Language Resources and System Evaluation)Annotation and evaluation of corpora; linguistic resources development methodologies, standards, tools and evaluations; crowd-sourcing; evaluations, systems and applications of human language technology;
Audio: Main Changes After long discussions of AATC with their final approval in June 2013; Expanding “Music IR” and Symbolic Processing
