Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used to extract valuable information from long text. However, in written Thai, there are no explicit sentence markers. Some prior works use machine learning; however, a deep learning approach has never been employed. We propose a deep learning model for sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate two techniques that allow us to utilize unlabeled data: Cross-View Training (CVT) as a semi-supervised learning technique, and a pre-trained language model (ELMo) to improve word representation. In the experiments, our model reduced the relative error by 7.4% and 18.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. Ablation studies revealed that the main contributing factor was adopting n-gram features, which were further analyzed using the interpretation technique and indicated that the model utilizes the features in the same way that humans do

Indonesian Sentence Boundary Detection using Deep Learning Approaches

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine sentence boundaries. Two labels will be used, namely O as a non-boundary token and E as the last token marker in the sentence. To do this, we used the Bi-LSTM approach, which has been widely used in sequence labeling. We have proved that our approach works for Indonesian text using pre-trained embedding in Indonesian, as in previous studies. This study achieved an F1-Score value of 98.49 percent. When compared to previous studies, the achieved performance represents a significant increase in outcomes.

Understanding the structure and meaning of Finnish texts: From corpus creation to deep language modelling

Natural Language Processing (NLP) is a cross-disciplinary field combining elements of computer science, artificial intelligence, and linguistics, with the objective of developing means for computational analysis, understanding or generation of human language. The primary aim of this thesis is to advance natural language processing in Finnish by providing more resources and investigating the most effective machine learning based practices for their use. The thesis focuses on NLP topics related to understanding the structure and meaning of written language, mainly concentrating on structural analysis (syntactic parsing) as well as exploring the semantic equivalence of statements that vary in their surface realization (paraphrase modelling). While the new resources presented in the thesis are developed for Finnish, most of the methodological contributions are language-agnostic, and the accompanying papers demonstrate the application and evaluation of these methods across multiple languages. The first set of contributions of this thesis revolve around the development of a state-of-the-art Finnish dependency parsing pipeline. Firstly, the necessary Finnish training data was converted to the Universal Dependencies scheme, integrating Finnish into this important treebank collection and establishing the foundations for Finnish UD parsing. Secondly, a novel word lemmatization method based on deep neural networks is introduced and assessed across a diverse set of over 50 languages. And finally, the overall dependency parsing pipeline is evaluated on a large number of languages, securing top ranks in two competitive shared tasks focused on multilingual dependency parsing. The overall outcome of this line of research is a parsing pipeline reaching state-of-the-art accuracy in Finnish dependency parsing, the parsing numbers obtained with the latest pre-trained language models approaching (at least near) human-level performance. The achievement of large language models in the area of dependency parsing— as well as in many other structured prediction tasks— brings up the hope of the large pre-trained language models genuinely comprehending language, rather than merely relying on simple surface cues. However, datasets designed to measure semantic comprehension in Finnish have been non-existent, or very scarce at the best. To address this limitation, and to reflect the general change of emphasis in the field towards task more semantic in nature, the second part of the thesis shifts its focus to language understanding through an exploration of paraphrase modelling. The second contribution of the thesis is the creation of a novel, large-scale, manually annotated corpus of Finnish paraphrases. A unique aspect of this corpus is that its examples have been manually extracted from two related text documents, with the objective of obtaining non-trivial paraphrase pairs valuable for training and evaluating various language understanding models on paraphrasing. We show that manual paraphrase extraction can yield a corpus featuring pairs that are both notably longer and less lexically overlapping than those produced through automated candidate selection, the current prevailing practice in paraphrase corpus construction. Another distinctive feature in the corpus is that the paraphrases are identified and distributed within their document context, allowing for richer modelling and novel tasks to be defined

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

Multilingual pre-trained models are able to zero-shot transfer knowledge from rich-resource to low-resource languages in machine reading comprehension (MRC). However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language. In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (SSDM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models. To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement. Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100. Code is available at:https://github.com/wulinjuan/SSDM_MRC.Comment: Accepted to ACL 2022 (main conference