Lecture 5: Part-Of-Speech Tagging
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How Do BERT Embeddings Organize Linguistic Knowledge?
How Do BERT Embeddings Organize Linguistic Knowledge? Giovanni Puccettiy , Alessio Miaschi? , Felice Dell’Orletta y Scuola Normale Superiore, Pisa ?Department of Computer Science, University of Pisa Istituto di Linguistica Computazionale “Antonio Zampolli”, Pisa ItaliaNLP Lab – www.italianlp.it [email protected], [email protected], [email protected] Abstract et al., 2019), we proposed an in-depth investigation Several studies investigated the linguistic in- aimed at understanding how the information en- formation implicitly encoded in Neural Lan- coded by BERT is arranged within its internal rep- guage Models. Most of these works focused resentation. In particular, we defined two research on quantifying the amount and type of in- questions, aimed at: (i) investigating the relation- formation available within their internal rep- ship between the sentence-level linguistic knowl- resentations and across their layers. In line edge encoded in a pre-trained version of BERT and with this scenario, we proposed a different the number of individual units involved in the en- study, based on Lasso regression, aimed at understanding how the information encoded coding of such knowledge; (ii) understanding how by BERT sentence-level representations is ar- these sentence-level properties are organized within ranged within its hidden units. Using a suite of the internal representations of BERT, identifying several probing tasks, we showed the existence groups of units more relevant for specific linguistic of a relationship between the implicit knowl- tasks. We defined a suite of probing tasks based on edge learned by the model and the number of a variable selection approach, in order to identify individual units involved in the encodings of which units in the internal representations of BERT this competence. -
Treebanks, Linguistic Theories and Applications Introduction to Treebanks
Treebanks, Linguistic Theories and Applications Introduction to Treebanks Lecture One Petya Osenova and Kiril Simov Sofia University “St. Kliment Ohridski”, Bulgaria Bulgarian Academy of Sciences, Bulgaria ESSLLI 2018 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Plan of the Lecture • Definition of a treebank • The place of the treebank in the language modeling • Related terms: parsebank, dynamic treebank • Prerequisites for the creation of a treebank • Treebank lifecycle • Theory (in)dependency • Language (in)dependency • Tendences in the treebank development 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Treebank Definition A corpus annotated with syntactic information • The information in the annotation is added/checked by a trained annotator - manual annotation • The annotation is complete - no unannotated fragments of the text • The annotation is consistent - similar fragments are analysed in the same way • The primary format of annotation - syntactic tree/graph 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Example Syntactic Trees from Wikipedia The two main approaches to modeling the syntactic information 30th European Summer School in Logic, Language and Information (6 August – 17 August 2018) Pros vs. Cons (Handbook of NLP, p. 171) Constituency • Easy to read • Correspond to common grammatical knowledge (phrases) • Introduce arbitrary complexity Dependency • Flexible • Also correspond to common grammatical -
The Role of Higher-Level Linguistic Features in HMM-Based Speech Synthesis
INTERSPEECH 2010 The role of higher-level linguistic features in HMM-based speech synthesis Oliver Watts, Junichi Yamagishi, Simon King Centre for Speech Technology Research, University of Edinburgh, UK [email protected] [email protected] [email protected] Abstract the annotation of a synthesiser’s training data on listeners’ re- action to the speech produced by that synthesiser is still unclear We analyse the contribution of higher-level elements of the lin- to us. In particular, we suspect that features such as pitch ac- guistic specification of a data-driven speech synthesiser to the cent type which might be useful in voice-building if labelled naturalness of the synthetic speech which it generates. The reliably, are under-used in conventional systems because of la- system is trained using various subsets of the full feature-set, belling/prediction errors. For building the systems to be evalu- in which features relating to syntactic category, intonational ated we therefore used data for which hand labelled annotation phrase boundary, pitch accent and boundary tones are selec- of ToBI events is available. This allows us to compare ideal sys- tively removed. Utterances synthesised by the different config- tems built from a corpus where these higher-level features are urations of the system are then compared in a subjective evalu- accurately annotated with more conventional systems that rely ation of their naturalness. exclusively on prediction of these features from text for annota- The work presented forms background analysis for an on- tion of their training data. going set of experiments in performing text-to-speech (TTS) conversion based on shallow features: features that can be triv- 2. -
Senserelate::Allwords - a Broad Coverage Word Sense Tagger That Maximizes Semantic Relatedness
WordNet::SenseRelate::AllWords - A Broad Coverage Word Sense Tagger that Maximizes Semantic Relatedness Ted Pedersen and Varada Kolhatkar Department of Computer Science University of Minnesota Duluth, MN 55812 USA tpederse,kolha002 @d.umn.edu http://senserelate.sourceforge.net{ } Abstract Despite these difficulties, word sense disambigua- tion is often a necessary step in NLP and can’t sim- WordNet::SenseRelate::AllWords is a freely ply be ignored. The question arises as to how to de- available open source Perl package that as- velop broad coverage sense disambiguation modules signs a sense to every content word (known that can be deployed in a practical setting without in- to WordNet) in a text. It finds the sense of vesting huge sums in manual annotation efforts. Our each word that is most related to the senses answer is WordNet::SenseRelate::AllWords (SR- of surrounding words, based on measures found in WordNet::Similarity. This method is AW), a method that uses knowledge already avail- shown to be competitive with results from re- able in the lexical database WordNet to assign senses cent evaluations including SENSEVAL-2 and to every content word in text, and as such offers SENSEVAL-3. broad coverage and requires no manual annotation of training data. SR-AW finds the sense of each word that is most 1 Introduction related or most similar to those of its neighbors in the Word sense disambiguation is the task of assigning sentence, according to any of the ten measures avail- a sense to a word based on the context in which it able in WordNet::Similarity (Pedersen et al., 2004). -
Deep Linguistic Analysis for the Accurate Identification of Predicate
Deep Linguistic Analysis for the Accurate Identification of Predicate-Argument Relations Yusuke Miyao Jun'ichi Tsujii Department of Computer Science Department of Computer Science University of Tokyo University of Tokyo [email protected] CREST, JST [email protected] Abstract obtained by deep linguistic analysis (Gildea and This paper evaluates the accuracy of HPSG Hockenmaier, 2003; Chen and Rambow, 2003). parsing in terms of the identification of They employed a CCG (Steedman, 2000) or LTAG predicate-argument relations. We could directly (Schabes et al., 1988) parser to acquire syntac- compare the output of HPSG parsing with Prop- tic/semantic structures, which would be passed to Bank annotations, by assuming a unique map- statistical classifier as features. That is, they used ping from HPSG semantic representation into deep analysis as a preprocessor to obtain useful fea- PropBank annotation. Even though PropBank tures for training a probabilistic model or statistical was not used for the training of a disambigua- tion model, an HPSG parser achieved the ac- classifier of a semantic argument identifier. These curacy competitive with existing studies on the results imply the superiority of deep linguistic anal- task of identifying PropBank annotations. ysis for this task. Although the statistical approach seems a reason- 1 Introduction able way for developing an accurate identifier of Recently, deep linguistic analysis has successfully PropBank annotations, this study aims at establish- been applied to real-world texts. Several parsers ing a method of directly comparing the outputs of have been implemented in various grammar for- HPSG parsing with the PropBank annotation in or- malisms and empirical evaluation has been re- der to explicitly demonstrate the availability of deep ported: LFG (Riezler et al., 2002; Cahill et al., parsers. -
The Procedure of Lexico-Semantic Annotation of Składnica Treebank
The Procedure of Lexico-Semantic Annotation of Składnica Treebank Elzbieta˙ Hajnicz Institute of Computer Science, Polish Academy of Sciences ul. Ordona 21, 01-237 Warsaw, Poland [email protected] Abstract In this paper, the procedure of lexico-semantic annotation of Składnica Treebank using Polish WordNet is presented. Other semantically annotated corpora, in particular treebanks, are outlined first. Resources involved in annotation as well as a tool called Semantikon used for it are described. The main part of the paper is the analysis of the applied procedure. It consists of the basic and correction phases. During basic phase all nouns, verbs and adjectives are annotated with wordnet senses. The annotation is performed independently by two linguists. Multi-word units obtain special tags, synonyms and hypernyms are used for senses absent in Polish WordNet. Additionally, each sentence receives its general assessment. During the correction phase, conflicts are resolved by the linguist supervising the process. Finally, some statistics of the results of annotation are given, including inter-annotator agreement. The final resource is represented in XML files preserving the structure of Składnica. Keywords: treebanks, wordnets, semantic annotation, Polish 1. Introduction 2. Semantically Annotated Corpora It is widely acknowledged that linguistically annotated cor- Semantic annotation of text corpora seems to be the last pora play a crucial role in NLP. There is even a tendency phase in the process of corpus annotation, less popular than towards their ever-deeper annotation. In particular, seman- morphosyntactic and (shallow or deep) syntactic annota- tically annotated corpora become more and more popular tion. However, there exist semantically annotated subcor- as they have a number of applications in word sense disam- pora for many languages. -
Unified Language Model Pre-Training for Natural
Unified Language Model Pre-training for Natural Language Understanding and Generation Li Dong∗ Nan Yang∗ Wenhui Wang∗ Furu Wei∗† Xiaodong Liu Yu Wang Jianfeng Gao Ming Zhou Hsiao-Wuen Hon Microsoft Research {lidong1,nanya,wenwan,fuwei}@microsoft.com {xiaodl,yuwan,jfgao,mingzhou,hon}@microsoft.com Abstract This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirec- tional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-of- the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. 1 Introduction Language model (LM) pre-training has substantially advanced the state of the art across a variety of natural language processing tasks [8, 29, 19, 31, 9, 1]. -
The RACAI Text-To-Speech Synthesis System
The RACAI Text-to-Speech Synthesis System Tiberiu Boroș, Radu Ion, Ștefan Daniel Dumitrescu Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy (RACAI) [email protected], [email protected], [email protected] of standalone Natural Language Processing (NLP) Tools Abstract aimed at enabling text-to-speech (TTS) synthesis for less- This paper describes the RACAI Text-to-Speech (TTS) entry resourced languages. A good example is the case of for the Blizzard Challenge 2013. The development of the Romanian, a language which poses a lot of challenges for TTS RACAI TTS started during the Metanet4U project and the synthesis mainly because of its rich morphology and its system is currently part of the METASHARE platform. This reduced support in terms of freely available resources. Initially paper describes the work carried out for preparing the RACAI all the tools were standalone, but their design allowed their entry during the Blizzard Challenge 2013 and provides a integration into a single package and by adding a unit selection detailed description of our system and future development speech synthesis module based on the Pitch Synchronous directions. Overlap-Add (PSOLA) algorithm we were able to create a fully independent text-to-speech synthesis system that we refer Index Terms: speech synthesis, unit selection, concatenative to as RACAI TTS. 1. Introduction A considerable progress has been made since the start of the project, but our system still requires development and Text-to-speech (TTS) synthesis is a complex process that although considered premature, our participation in the addresses the task of converting arbitrary text into voice. -
Synthesis and Recognition of Speech Creating and Listening to Speech
ISSN 1883-1974 (Print) ISSN 1884-0787 (Online) National Institute of Informatics News NII Interview 51 A Combination of Speech Synthesis and Speech Oct. 2014 Recognition Creates an Affluent Society NII Special 1 “Statistical Speech Synthesis” Technology with a Rapidly Growing Application Area NII Special 2 Finding Practical Application for Speech Recognition Feature Synthesis and Recognition of Speech Creating and Listening to Speech A digital book version of “NII Today” is now available. http://www.nii.ac.jp/about/publication/today/ This English language edition NII Today corresponds to No. 65 of the Japanese edition [Advance Notice] Great news! NII Interview Yamagishi-sensei will create my voice! Yamagishi One result is a speech translation sys- sound. Bit (NII Character) A Combination of Speech Synthesis tem. This system recognizes speech and translates it Ohkawara I would like your comments on the fu- using machine translation to synthesize speech, also ture challenges. and Speech Recognition automatically translating it into every language to Ono The challenge for speech recognition is how speak. Moreover, the speech is created with a human close it will come to humans in the distant speech A Word from the Interviewer voice. In second language learning, you can under- case. If this study is advanced, it will be possible to Creates an Affluent Society stand how you should pronounce it with your own summarize the contents of a meeting and to automati- voice. If this is further advanced, the system could cally take the minutes. If a robot understands the con- have an actor in a movie speak in a different language tents of conversations by multiple people in a natural More and more people have begun to use smart- a smartphone, I use speech input more often. -
Building a Treebank for French
Building a treebank for French £ £¥ Anne Abeillé£ , Lionel Clément , Alexandra Kinyon ¥ £ TALaNa, Université Paris 7 University of Pennsylvania 75251 Paris cedex 05 Philadelphia FRANCE USA abeille, clement, [email protected] Abstract Very few gold standard annotated corpora are currently available for French. We present an ongoing project to build a reference treebank for French starting with a tagged newspaper corpus of 1 Million words (Abeillé et al., 1998), (Abeillé and Clément, 1999). Similarly to the Penn TreeBank (Marcus et al., 1993), we distinguish an automatic parsing phase followed by a second phase of systematic manual validation and correction. Similarly to the Prague treebank (Hajicova et al., 1998), we rely on several types of morphosyntactic and syntactic annotations for which we define extensive guidelines. Our goal is to provide a theory neutral, surface oriented, error free treebank for French. Similarly to the Negra project (Brants et al., 1999), we annotate both constituents and functional relations. 1. The tagged corpus pronoun (= him ) or a weak clitic pronoun (= to him or to As reported in (Abeillé and Clément, 1999), we present her), plus can either be a negative adverb (= not any more) the general methodology, the automatic tagging phase, the or a simple adverb (= more). Inflectional morphology also human validation phase and the final state of the tagged has to be annotated since morphological endings are impor- corpus. tant for gathering constituants (based on agreement marks) and also because lots of forms in French are ambiguous 1.1. Methodology with respect to mode, person, number or gender. For exam- 1.1.1. -
The Role of Speech Processing in Human-Computer Intelligent Communication
THE ROLE OF SPEECH PROCESSING IN HUMAN-COMPUTER INTELLIGENT COMMUNICATION Candace Kamm, Marilyn Walker and Lawrence Rabiner Speech and Image Processing Services Research Laboratory AT&T Labs-Research, Florham Park, NJ 07932 Abstract: We are currently in the midst of a revolution in communications that promises to provide ubiquitous access to multimedia communication services. In order to succeed, this revolution demands seamless, easy-to-use, high quality interfaces to support broadband communication between people and machines. In this paper we argue that spoken language interfaces (SLIs) are essential to making this vision a reality. We discuss potential applications of SLIs, the technologies underlying them, the principles we have developed for designing them, and key areas for future research in both spoken language processing and human computer interfaces. 1. Introduction Around the turn of the twentieth century, it became clear to key people in the Bell System that the concept of Universal Service was rapidly becoming technologically feasible, i.e., the dream of automatically connecting any telephone user to any other telephone user, without the need for operator assistance, became the vision for the future of telecommunications. Of course a number of very hard technical problems had to be solved before the vision could become reality, but by the end of 1915 the first automatic transcontinental telephone call was successfully completed, and within a very few years the dream of Universal Service became a reality in the United States. We are now in the midst of another revolution in communications, one which holds the promise of providing ubiquitous service in multimedia communications. -
Converting an HPSG-Based Treebank Into Its Parallel Dependency-Based Treebank
Converting an HPSG-based Treebank into its Parallel Dependency-based Treebank Masood Ghayoomiy z Jonas Kuhnz yDepartment of Mathematics and Computer Science, Freie Universität Berlin z Institute for Natural Language Processing, University of Stuttgart [email protected] [email protected] Abstract A treebank is an important language resource for supervised statistical parsers. The parser induces the grammatical properties of a language from this language resource and uses the model to parse unseen data automatically. Since developing such a resource is very time-consuming and tedious, one can take advantage of already extant resources by adapting them to a particular application. This reduces the amount of human effort required to develop a new language resource. In this paper, we introduce an algorithm to convert an HPSG-based treebank into its parallel dependency-based treebank. With this converter, we can automatically create a new language resource from an existing treebank developed based on a grammar formalism. Our proposed algorithm is able to create both projective and non-projective dependency trees. Keywords: Treebank Conversion, the Persian Language, HPSG-based Treebank, Dependency-based Treebank 1. Introduction treebank is introduced in Section 4. The properties Supervised statistical parsers require a set of annotated of the available dependency treebanks for Persian and data, called a treebank, to learn the grammar of the their pros and cons are explained in Section 5. The target language and create a wide coverage grammar tools and the experimental setup as well as the ob- model to parse unseen data. Developing such a data tained results are described and discussed in Section source is a tedious and time-consuming task.