ymoslem/acl-abstracts huggingface dataset:
Speech Timing in Typically Developing Mandarin-Speaking Children From Ages 3 To 4 (2022)

This study aims to develop a better understanding of the speech timing development in Mandarin-speaking children from 3 to 4 years of age. Data were selected from two typically developing children. Four 50-min recordings were collected during 3 and 4 years old based on natural conversation among the observers, participants, and the parents, and the picture-naming task. Speech timing were measured by Praat, including speaking rate, articulation rate, mean length of utterance (MLUs), mean utterance duration, mean word duration, pause ratio, and volubility. Major findings of the current study are: 1) Five measurements (speaking rate, mean length of utterance(MLUs), mean utterance length, mean word duration and volubility) decreased with age in both children; 2) Articulation rate of both children increased with age; 3) Comparing with the findings from previous studies, pause ratio of both slightly increased with age. These findings not only contribute to a more comprehensive data for assessment, it also can be a reference in speech intervention.
Right-Dominant Tones in Zhangzhou: On and Through Phonetic Surface (2022)

This study conducts a systematic acoustic exploration into the phonetic nature of rightmost tones in a right-dominant tone sandhi system based on empirical data from 21 native speakers of Zhangzhou Southern Min, which presents eight tonal contrasts at the underlying level. The results reveal that, (a) the F0 contour shape realisation of rightmost tones in Zhangzhou appears not to be categorically affected by their preceding tones. (b) Seven out of eight rightmost tones have two statistically significantly different variants in their F0 onset realisation, indicating their regressive sensitivity to the offset phonetics of preceding tones. (c) The forms of rightmost tones are not straightforward related to their counterparts in citation. Instead, two versions of the F0 system can be identified, with the unmarked forms resembling their citation values and the marked forms occurring as a consequence of the phonetic impact of their preceding tones and the F0-declining effect of utterance-final position. (d) The phonetic variation of rightmost tones reflects the across-linguistic tendency of tonal articulation in connected speech but contradicts the default principle for identifying the right dominance of tone sandhi in Sinitic languages.
Web-API-Based Chatbot Generation with Analysis and Expansion for Training Sentences (2022)

With Web API technology becoming increasingly mature, how to integrate Web API and Chatbot technology has become an issue of great interest. This study plans to build a semi-automatic method and tool, BOTEN. This method allows application developers to build Chatbot interfaces with specified Web APIs quickly. To ensure that the Chatbot has sufficient natural language understanding (NLU) capability, this research evaluates the training sentences written by the developer through TF-IDF, WordNet, and SpaCy techniques, and suggests the developer modify the training sentences with poor quality. This technique can also be used to automatically increase the number of training sentences to improve the capability of Intent recognition.
The Design and Development of a System for Chinese Character Difficulty and Features (2022)

Feature analysis of Chinese characters plays a prominent role in {\textquotedblleft}character-based{\textquotedblright} education. However, there is an urgent need for a text analysis system for processing the difficulty of composing components for characters, primarily based on Chinese learners' performance. To meet this need, the purpose of this research was to provide such a system by adapting a data-driven approach. Based on Chen et al.`s (2011) Chinese Orthography Database, this research has designed and developed an system: Character Difficulty - Research on Multi-features (CD-ROM). This system provides three functions: (1) analyzing a text and providing its difficulty regarding Chinese characters; (2) decomposing characters into components and calculating the frequency of components based on the analyzed text; and (3) affording component-deriving characters based on the analyzed text and downloadable images as teaching materials. With these functions highlighting multi-level features of characters, this system has the potential to benefit the fields of Chinese character instruction, Chinese orthographic learning, and Chinese natural language processing.
Image Caption Generation for Low-Resource Assamese Language (2022)

Image captioning is a prominent Artificial Intelligence (AI) research area that deals with visual recognition and a linguistic description of the image. It is an interdisciplinary field concerning how computers can see and understand digital images{\&} videos, and describe them in a language known to humans. Constructing a meaningful sentence needs both structural and semantic information of the language. This paper highlights the contribution of image caption generation for the Assamese language. The unavailability of an image caption generation system for the Assamese language is an open problem for AI-NLP researchers, and it`s just an early stage of the research. To achieve our defined objective, we have used the encoder-decoder framework, which combines the Convolutional Neural Networks and the Recurrent Neural Networks. The experiment has been tested on Flickr30k and Coco Captions dataset, which have been originally present in the English language. We have translated these datasets into Assamese language using the state-of-the-art Machine Translation (MT) system for our designed work.
Building an Enhanced Autoregressive Document Retriever Leveraging Supervised Contrastive Learning (2022)

The goal of an information retrieval system is to retrieve documents that are most relevant to a given user query from a huge collection of documents, which usually requires time-consuming multiple comparisons between the query and candidate documents so as to find the most relevant ones. Recently, a novel retrieval modeling approach, dubbed Differentiable Search Index (DSI), has been proposed. DSI dramatically simplifies the whole retrieval process by encoding all information about the document collection into the parameter space of a single Transformer model, on top of which DSI can in turn generate the relevant document identities (IDs) in an autoregressive manner in response to a user query. Although DSI addresses the shortcomings of traditional retrieval systems, previous studies have pointed out that DSI might fail to retrieve relevant documents because DSI uses the document IDs as the pivotal mechanism to establish the relationship between queries and documents, whereas not every document in the document collection has its corresponding relevant and irrelevant queries for the training purpose. In view of this, we put forward to leveraging supervised contrastive learning to better render the relationship between queries and documents in the latent semantic space. Furthermore, an approximate nearest neighbor search strategy is employed at retrieval time to further assist the Transformer model in generating document IDs relevant to a posed query more efficiently. A series of experiments conducted on the Nature Question benchmark dataset confirm the effectiveness and practical feasibility of our approach in relation to some strong baseline systems.
A Quantitative Analysis of Comparison of Emoji Sentiment: Taiwan Mandarin Users and English Users (2022)

Emojis have become essential components in our digital communication. Emojis, especially smiley face emojis and heart emojis, are considered the ones conveying more emotions. In this paper, two functions of emoji usages are discussed across two languages, Taiwanese Mandarin and English. The first function discussed here is sentiment enhancement and the other is sentiment modification. Multilingual language model is adopted for seeing the probability distribution of the text sentiment, and relative entropy is used to quantify the degree of changes. The results support the previous research that emojis are more frequently-used in positive contexts, smileys tend to be used for expressing emotions and prove the language-independent nature of emojis.
Applying Information Extraction to Storybook Question and Answer Generation (2022)

For educators, how to generate high quality question-answer pairs from story text is a time-consuming and labor-intensive task. The purpose is not to make students unable to answer, but to ensure that students understand the story text through the generated question-answer pairs. In this paper, we improve the FairyTaleQA question generation method by incorporating question type and its definition to the input for fine-tuning the BART (Lewis et al., 2020) model. Furthermore, we make use of the entity and relation extraction from (Zhong and Chen, 2021) as an element of template-based question generation.
Improving Response Diversity through Commonsense-Aware Empathetic Response Generation (2022)

Due to the lack of conversation practice, the main challenge for the second-language learners is speaking. Our goal is to develop a chatbot to encourage individuals to reflect, describe, analyse and communicate what they read as well as improve students' English expression skills. In this paper, we exploit COMMET, an inferential commonsense knowledge generator, as the background knowledge to improve the generation diversity. We consider two approaches to increase the diversity of empathetic response generation. For nonpretrained models, We apply AdaLabel (Wang et al., 2021) to Commonsense-aware Empathetic model (Sabour et al., 2022) and improve Distinct-2 score from 2.99 to 4.08 on EMPATHETIC DIALOGUES (ED). Furthermore, we augment the pretrained BART model with various commonsense knowledge to generate more informative empathetic responses. Not only has the automatic evaluation of distinct-2 scores improved from 9.11 to 11.21, but the manual case study also shows that CE-BART significantly outperform CEM-AdaLabel.
A Preliminary Study on Mandarin-Hakka neural machine translation using small-sized data (2022)

In this study, we implemented a machine translation system using the Convolutional Neural Network with Attention mechanism for translating Mandarin to Sixan-accent Hakka. Specifically, to cope with the different idioms or terms used between Northern and Southern Sixan-accent, we analyzed the corpus differences and lexicon definition, and then separated the various word usages for training exclusive models for each accent. Besides, since the collected Hakka corpora are relatively limited, the unseen words frequently occurred during real-world translation. In our system, we selected suitable thresholds for each model based on the model verification to reject non-suitable translated words. Then, by applying the proposed algorithm, which adopted the forced Hakka idioms/terms segmentation and the common Mandarin word substitution, the resultant translation sentences become more intelligible. Therefore, the proposed system achieved promising results using small-sized data. This system could be used for Hakka language teaching and also the front-end of Mandarin and Hakka code-switching speech synthesis systems.
NCU1415 at ROCLING 2022 Shared Task: A light-weight transformer-based approach for Biomedical Name Entity Recognition (2022)

Name Entity Recognition (NER) is a very important and basic task in traditional NLP tasks. In the biomedical field, NER tasks have been widely used in various products developed by various manufacturers. These include parsing, QA system, key information extraction or replacement in dialogue systems, and the practical application of knowledge parsing. In different fields, including bio-medicine, communication technology, e-commerce etc., NER technology is needed to identify drugs, diseases, commodities and other objects. This implementation focuses on the CLING 2022 SHARED TASK`s(Lee et al. 2022) NER TASK in biomedical field, with a bit of tuning and experimentation based on the language models.
CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training (2022)

This study uses training and validation data from the {\textquotedblleft}ROCLING 2022 Chinese Health Care Named Entity Recognition Task{\textquotedblright} for modeling. The modeling process adopts technologies such as data augmentation and data post-processing, and uses the MacBERT pre-training model to build a dedicated Chinese medical field NER recognizer. During the fine-tuning process, we also added adversarial training methods, such as FGM and PGD, and the results of the final tuned model were close to the best team for task evaluation. In addition, by introducing mixed-precision training, we also greatly reduce the time cost of training.
SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier (2022)

In this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, {\textquotedblleft}muscle{\textquotedblright} would be labeled {\textquotedblleft}B-BODY{\textquotedblright} and {\textquotedblleft}I-BODY{\textquotedblright}, and {\textquotedblleft}cough{\textquotedblright} would be {\textquotedblleft}B-SYMP{\textquotedblright} and {\textquotedblleft}I-SYMP{\textquotedblright}. All words outside the category are marked with {\textquotedblleft}O{\textquotedblright}. The Chinese HealthNER Corpus contains 30,692 sentences, of which 2531 sentences are divided into the validation set (dev) for this evaluation, and the conference finally provides another 3204 sentences for the test set (test). We use BLSTM{\_}CRF, Roberta+BLSTM{\_}CRF and BERT Classifier to submit three prediction results respectively. Finally, the BERT Classifier system submitted as RUN3 achieved the best prediction performance, with an accuracy of 80.18{\%}, a recall rate of 78.3{\%}, and an F1-score of 79.23.
YNU-HPCC at ROCLING 2022 Shared Task: A Transformer-based Model with Focal Loss and Regularization Dropout for Chinese Healthcare Named Entity Recognition (2022)

Named Entity Recognition (NER) is a fundamental task in information extraction that locates the mentions of named entities and classifies them in unstructured texts. Previous studies typically used hidden Markov model (HMM) and conditional random fields (CRF) for NER. To learn long-distance dependencies in text, recurrent neural networks, e.g., LSTM and GRU can extract the semantic features for each token with a sequential manner. Based on Transformers, this paper describes the contribution to ROCLING-2022 Share Task. This paper adopts a transformer-based model with focal Loss and regularization dropout. The focal loss is to overcome the uneven distribution of the label. The regularization dropout (r-drop) is to address the problem of vocabulary and descriptions that are too domain-specific. The ensemble learning is to improve the performance of the model. Comparative experiments were conducted on dev set to select the model with the best performance for submission. That is, BERT model with BiLSTM-CRF, focal loss and R-Drop has achieved the best F1-score of 0.7768 and rank the 4th place.
NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach (2022)

ROCLING 2022 shared task is to design a method that can tag medical entities in sentences and then classify them into categories through an algorithm. This paper proposes three models to deal with NER issues. The first is a BERT model combined with a classifier. The second is a two-stage model, where the first stage is to use a BERT model combined with a classifier for detecting whether medical entities exist in a sentence, and the second stage focuses on classifying the entities into categories. The third approach is to combine the first two models and a model based on the lexicon approach, integrating the outputs of the three models and making predictions. The prediction results of the three models for the validation and testing datasets show little difference in the performance of the three models, with the best performance on the F1 indicator being 0.7569 for the first model.
SCU-NLP at ROCLING 2022 Shared Task: Experiment and Error Analysis of Biomedical Entity Detection Model (2022)

Named entity recognition generally refers to entities with specific meanings in unstructured text, including names of people, places, organizations, dates, times, quantities, proper nouns and other words. In the medical field, it may be drug names, Organ names, test items, nutritional supplements, etc. The purpose of named entity recognition in this study is to search for the above items from unstructured input text. In this study, taking healthcare as the research purpose, and predicting named entity boundaries and categories of sentences based on ten entity types, We explore multiple fundamental NER approaches to solve this task, Include: Hidden Markov Models, Conditional Random Fields, Random Forest Classifier and BERT. The prediction results are more significant in the F-score of the CRF model, and have achieved better results.
MIGBaseline at ROCLING 2022 Shared Task: Report on Named Entity Recognition Using Chinese Healthcare Datasets (2022)

Named Entity Recognition (NER) tools have been in development for years, yet few have been aimed at medical documents. The increasing needs for analyzing medical data makes it crucial to build a sophisticated NER model for this missing area. In this paper, W2NER, the state-of-the-art NER model, which has excelled in English and Chinese tasks, is run through selected inputs, several pretrained language models, and training strategies. The objective was to build an NER model suitable for healthcare corpora in Chinese. The best model managed to achieve an F1 score at 81.93{\%}, which ranked first in the ROCLING 2022 shared task.
Overview of the ROCLING 2022 Shared Task for Chinese Healthcare Named Entity Recognition (2022)

This paper describes the ROCLING-2022 shared task for Chinese healthcare named entity recognition, including task description, data preparation, performance metrics, and evaluation results. Among ten registered teams, seven participating teams submitted a total of 20 runs. This shared task reveals present NLP techniques for dealing with Chinese named entity recognition in the healthcare domain. All data sets with gold standards and evaluation scripts used in this shared task are publicly available for future research.
A First Attempt at Unreliable News Detection in Swedish (2022)

Throughout the COVID-19 pandemic, a parallel infodemic has also been going on such that the information has been spreading faster than the virus itself. During this time, every individual needs to access accurate news in order to take corresponding protective measures, regardless of their country of origin or the language they speak, as misinformation can cause significant loss to not only individuals but also society. In this paper we train several machine learning models (ranging from traditional machine learning to deep learning) to try to determine whether news articles come from either a reliable or an unreliable source, using just the body of the article. Moreover, we use a previously introduced corpus of news in Swedish related to the COVID-19 pandemic for the classification task. Given that our dataset is both unbalanced and small, we use subsampling and easy data augmentation (EDA) to try to solve these issues. In the end, we realize that, due to the small size of our dataset, using traditional machine learning along with data augmentation yields results that rival those of transformer models such as BERT.
BanglaHateBERT: BERT for Abusive Language Detection in Bengali (2022)

This paper introduces BanglaHateBERT, a retrained BERT model for abusive language detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive, and hateful corpus that we have collected from different sources and made available to the public. Furthermore, we have collected and manually annotated 15K Bengali hate speech balanced dataset and made it publicly available for the research community. We used existing pre-trained BanglaBERT model and retrained it with 1.5 million offensive posts. We presented the results of a detailed comparison between generic pre-trained language model and retrained with the abuse-inclined version. In all datasets, BanglaHateBERT outperformed the corresponding available BERT model.
A Comparison of Machine Learning Techniques for Turkish Profanity Detection (2022)

Profanity detection became an important task with the increase of social media usage. Most of the users prefer a clean and profanity free environment to communicate with others. In order to provide a such environment for the users, service providers are using various profanity detection tools. In this paper, we researched on Turkish profanity detection in our search engine. We collected and labeled a dataset from search engine queries as one of the two classes: profane and not-profane. We experimented with several classical machine learning and deep learning methods and compared methods in means of speed and accuracy. We performed our best scores with transformer based Electra model with 0.93 F1 Score. We also compared our models with the state-of-the-art Turkish profanity detection tool and observed that we outperform it from all aspects.
Features and Categories of Hyperbole in Cyberbullying Discourse on Social Media (2022)

Cyberbullying discourse is achieved with multiple linguistic conveyances. Hyperboles witnessed in a corpus of cyberbullying utterances are studied. Linguistic features of hyperbole using the traditional grammatical indications of exaggerations are analyzed. The method relies on data selected from a larger corpus of utterances identified and labelled as {\textquotedblleft}bullying{\textquotedblright}, from Twitter, from October 2020 to March 2022. An outcome is a lexicon of 250 entries. A small number of lexical level features have been isolated, and chi-squared contingency tests applied to evaluating their information value in identifying hyperbole. Words or affixes indicating superlatives or extremes of scales, with positive but not negative valency items, interact with hyperbole classification in this data set. All utterances extracted has been considered exaggerations and the stylistic status of {\textquotedblleft}hyperbole{\textquotedblright} has been commented within the frame of new meanings in the context of social media.
Distributionally Robust Recurrent Decoders with Random Network Distillation (2022)

Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shift, which occurs in many practical applications when processing out-of-domain (OOD) text. This has been attributed to {\textquotedblleft}shortcut learning{\textquotedblright}{\textquotedblright}:'' relying on weak correlations over arbitrary large contexts. We propose a method based on OOD detection with Random Network Distillation to allow an autoregressive language model to automatically disregard OOD context during inference, smoothly transitioning towards a less expressive but more robust model as the data becomes more OOD, while retaining its full context capability when operating in-distribution. We apply our method to a GRU architecture, demonstrating improvements on multiple language modeling (LM) datasets.