pwc-archive/papers-with-abstracts huggingface dataset:
Data centric approach to Chinese Medical Speech Recognition
Authors: Sheng-Luen Chung, Yi-Shiuan Li, Hsien-Wei Ting
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Data Augmentation, speech-recognition, Speech Recognition

Concerning the development of Chinese medical speech recognition technology, this study re-addresses earlier encountered issues in accordance with the process of Machine Learning Engineering for Production (MLOps) from a data centric perspective. First is the new segmentation of speech utterances to meet sentences completeness for all utterances in the collected Chinese Medical Speech Corpus (ChiMeS). Second is optimization of Joint CTC/Attention model through data augmentation in boosting recognition performance out of very limited speech corpus. Overall, to facilitate the development of Chinese medical speech recognition, this paper contributes: (1) The ChiMeS corpus, the first Chinese Medicine Speech corpus of its kind, which is 14.4 hours, with a total of 7,225 sentences. (2) A trained Joint CTC/Attention ASR model by ChiMeS-14, yielding a Character Error Rate (CER) of 13.65% and a Keyword Error Rate (KER) of 20.82%, respectively, when tested on the ChiMeS-14 testing set. And (3) an evaluation platform set up to compare performance of other ASR models. All the released resources can be found in the ChiMeS portal (https://iclab.ee.ntust.edu.tw/home).
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems
Authors: Hou-An Lin, Chia-Ping Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Language Modeling, Language Modelling, Multi-Task Learning, speech-recognition, Speech Recognition, Transfer Learning

In this paper, we investigate how to use limited code-switching data to implement a code-switching speech recognition system. We utilize the Transformer end-to-end model to develop our code switching speech recognition system, which is trained with the Mandarin dataset and a small amount of Mandarin-English code switching dataset, as the baseline of this paper. Next, we compare the performance of systems after adding multi-task learning and transfer learning. Character Error Rate(CER) is adopted as the criterion for the system. Finally, we combined the three systems with the language model, respectively, our best result dropped to 23.9% compared with the baseline of 28.7%.
Discussion on domain generalization in the cross-device speaker verification system
Authors: Wei-Ting Lin, Yu-jia Zhang, Chia-Ping Chen, Chung-Li Lu, Bo-Cheng Chan
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Domain Generalization, Speaker Verification

In this paper, we use domain generalization to improve the performance of the cross-device speaker verification system. Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters. First, we use the VoxCeleb2 dataset to train ECAPA-TDNN as a baseline model. Then, use the CHT-TDSV dataset and the following domain generalization algorithms to fine-tune it: DANN, CDNN, Deep CORAL. Our proposed system tests 10 different scenarios in the NSYSU-TDSV dataset, including a single device and multiple devices. Finally, in the scenario of multiple devices, the best equal error rate decreased from 18.39 in the baseline to 8.84. Successfully achieved cross-device identification on the speaker verification system.
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning
Authors: You-Sheng Tsao, Tien-Hong Lo, Jiun-Ting Li, Shi-Yan Weng, Berlin Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Classification, Data Augmentation, Environmental Sound Classification, Sound Classification, Transfer Learning

With the widespread commercialization of smart devices, research on environmental sound classification has gained more and more attention in recent years. In this paper, we set out to make effective use of large-scale audio pretrained model and semi-supervised model training paradigm for environmental sound classification. To this end, an environmental sound classification method is first put forward, whose component model is built on top a large-scale audio pretrained model. Further, to simulate a low-resource sound classification setting where only limited supervised examples are made available, we instantiate the notion of transfer learning with a recently proposed training algorithm (namely, FixMatch) and a data augmentation method (namely, SpecAugment) to achieve the goal of semi-supervised model training. Experiments conducted on bench-mark dataset UrbanSound8K reveal that our classification method can lead to an accuracy improvement of 2.4% in relation to a current baseline method.
Mining Commonsense and Domain Knowledge from Math Word Problems
Authors: Shih-hung Tsai, Chao-Chun Liang, Hsin-Min Wang, Keh-Yih Su
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Math

Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
RCRNN-based Sound Event Detection System with Specific Speech Resolution
Authors: Sung-Jen Huang, Yih-Wen Wang, Chia-Ping Chen, Chung-Li Lu, Bo-Cheng Chan
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Data Augmentation, Event Detection, Sound Event Detection

Sound event detection (SED) system outputs sound events and their time boundaries in audio signals. We proposed an RCRNN-based SED system with residual connection and convolution block attention mechanism based on the mean-teacher framework of semi-supervised learning. The neural network can be trained with an amount of weakly labeled data and unlabeled data. In addition, we consider that the speech event has more information than other sound events. Thus, we use the specific time-frequency resolution to extract the acoustic feature of the speech event. Furthermore, we apply data augmentation and post-processing to improve the performance. On the DCASE 2021 Task 4 validation set, the proposed system achieves the PSDS (Poly-phonic Sound Event Detection Score)-scenario 2 of 57.6% and event-based F1-score of 41.6%, outperforming the baseline score of 52.7% and 40.7%.
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection
Authors: Hsin-Wei Wang, Bi-Cheng Yan, Yung-Chang Hsu, Berlin Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

There has been increasing demand to develop effective computer-assisted language training (CAPT) systems, which can provide feedback on mispronunciations and facilitate second-language (L2) learners to improve their speaking proficiency through repeated practice. Due to the shortage of non-native speech for training the automatic speech recognition (ASR) module of a CAPT system, the corresponding mispronunciation detection performance is often affected by imperfect ASR. Recognizing this importance, we in this paper put forward a two-stage mispronunciation detection method. In the first stage, the speech uttered by an L2 learner is processed by an end-to-end ASR module to produce N-best phone sequence hypotheses. In the second stage, these hypotheses are fed into a pronunciation model which seeks to faithfully predict the phone sequence hypothesis that is most likely pronounced by the learner, so as to improve the performance of mispronunciation detection. Empirical experiments conducted a English benchmark dataset seem to confirm the utility of our method.
Predicting elders’ cognitive flexibility from their language use
Authors: Man-Ying Wang, Yu-An Ko, Chin-Lan Huang, Jyun-Hong Chen, Te-Tien Ting
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10

This study recruited 51 elders aged 53-74 to discuss their daily activities in focus groups. The transcribed discourse was analyzed using the Chinese version of LIWC (Lin et al., 2020; Pennebaker et al., 2015) for cognitive complexity and dynamic language as well as content words related to elders’ daily activities. The interruption behavior during the conversation was also coded and analyzed. After controlling for education, gender and age, the results showed that cognitive flexibility performance was accompanied by the increasing adoption of dynamic language, insight words and family words. These findings serve as the basis for predicting elders’ cognitive flexibility through their daily language use.
Improve Chit-Chat and QA Sentence Classification in User Messages of Dialogue System using Dialogue Act Embedding
Authors: Chi Hsiang Chao, Xi Jie Hou, Yu Ching Chiu
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Sentence, Sentence Classification

In recent years, dialogue system is booming and widely used in customer service system, and has achieved good results. Viewing the conversation records between users and real customer service, we can see that the user’s sentences are mixed with questions about products and services, and chat with customer service. According to the experience of professionals, it is helpful in improving the user experience to mix some chats in customer service conversations. However, users’ questions are expected to be answered, while chatting is expected to interact with customer service. In order to produce an appropriate response, the dialogue system must be able to distinguish these two intentions effectively. Dialog act is a classification that linguists define according to its function. We think this information will help distinguishing questioning sentences and chatting sentences. In this paper, we combine a published COVID-19 QA dataset and a COVID-19-topic chat dataset to form our experimental data. Based on the BERT (Bidirectional Encoder Representation from Transformers) model, we build a question-chat classifier model. The experimental results show that the accuracy of the configuration with dialog act embedding is 16% higher than that with only original statement embedding. In addition, it is found that conversation behavior types such as “Statement-non-opinion”, “Signal-non-understanding” and “Appreciation” are more related to question sentences, while “Wh-Question”, “Yes-No-Question” and “Rhetorical-Question” questions are more related to chat sentences.
Data Augmentation Technology for Dysarthria Assistive Systems
Authors: Wei-Chung Chu, Ying-Hsiu Hung, Wei-Zhong Zheng, Ying-Hui Lai
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Data Augmentation

Voice-driven communication aids are one of the methods commonly used by patients with dysarthria. However, this type of assistive devices demands a large amount of voice data from patients to increase the effectiveness. In the meantime, this will sink patients into an overwhelming recording burden. Due to those difficulties, this research proposes a voice augmentation system to conquer the aforementioned concern. Furthermore, the system can improve the recognition efficiency. The results of this research reveal that the proposed speech generator system for dysarthria can launch corpus to be more similarities to the patient’s speech. Moreover, the recognition rate, in duplicate sentences, has been improved and promoted to the higher level. The word error rate can be reduced from 64.42% to 4.39% in the case of patients with Free-talk. According to these results, our proposed system can provide more reliable and helpful technique for the development of communication aids.
A Survey of Approaches to Automatic Question Generation:from 2019 to Early 2021
Authors: Chao-Yi Lu, Sin-En Lu
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Question Generation, Question-Generation

To provide analysis of recent researches of automatic question generation from text,we surveyed 9 papers between 2019 to early 2021, retrieved from Paper with Code(PwC). Our research follows the survey reported by Kurdi et al.(2020), in which analysis of 93 papers from 2014 to early2019 are provided. We analyzed the 9papers from aspects including: (1) purpose of question generation, (2) generation method, and (3) evaluation. We found that recent approaches tend to rely on semantic information and Transformer-based models are attracting increasing interest since they are more efficient. On the other hand,since there isn’t any widely acknowledged automatic evaluation metric designed for question generation, researchers adopt metrics of other natural language processing tasks to compare different systems.
A BERT-based Siamese-structured Retrieval Model
Authors: Hung-Yun Chiang, Kuan-Yu Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Information Retrieval, model, Retrieval

Due to the development of deep learning, the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT). The goal of information retrieval is to search the most relevant results for the user’s query from a large set of documents. Although BERT-based retrieval models have shown excellent results in many studies, these models usually suffer from the need for large amounts of computations and/or additional storage spaces. In view of the flaws, a BERT-based Siamese-structured retrieval model (BESS) is proposed in this paper. BESS not only inherits the merits of pre-trained language models, but also can generate extra information to compensate the original query automatically. Besides, the reinforcement learning strategy is introduced to make the model more robust. Accordingly, we evaluate BESS on three public-available corpora, and the experimental results demonstrate the efficiency of the proposed retrieval model.
MMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis
Authors: Guan-Yuan Chen, Ya-Fen Yeh
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Aspect Category Sentiment Analysis, Meta-Learning, Multi-Task Learning, Sentiment Analysis

Aspect Category Sentiment Analysis (ACSA), which aims to identify fine-grained sentiment polarities of the aspect categories discussed in user reviews. ACSA is challenging and costly when conducting it into real-world applications, that mainly due to the following reasons: 1.) Labeling the fine-grained ACSA data is often labor-intensive. 2.) The aspect categories will be dynamically updated and adjusted with the development of application scenarios, which means that the data must be relabeled frequently. 3.) Due to the increase of aspect categories, the model must be retrained frequently to fast adapt to the newly added aspect category data. To overcome the above-mentioned problems, we introduce a novel Meta Multi-Task Learning (MMTL) approach, that frame ACSA tasks as a meta-learning problem (i.e., regarding aspect-category sentiment polarity classification problems as the different training tasks for meta-learning) to learn an ideal and shareable initialization for the multi-task learning model that can be adapted to new ACSA tasks efficiently and effectively. Experiment results show that the proposed approach significantly outperforms the strong pre-trained transformer-based baseline model, especially, in the case of less labeled fine-grained training data.
Home Appliance Review Research Via Adversarial Reptile
Authors: Tai-Jung Kan, Chia-Hui Chang, Hsiu-Min Chuang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Aspect Category Sentiment Classification, Meta-Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentiment Analysis, Sentiment Classification

For manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspect of the product are most discussed . In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We find show that the macro-f1 is improved from 68.6% (BERT fine tuned model) to 70.3% (p-value 0.04).
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings
Authors: Yen-Hao Huang, Ratana Pornvattanavichai, Fernando Henrique Calderon Alvarado, Yi-Shin Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Document Summarization, Multi-Document Summarization, Sentence

Information overload has been one of the challenges regarding information from the Internet. It is not a matter of information access, instead, the focus had shifted towards the quality of the retrieved data. Particularly in the news domain, multiple outlets report on the same news events but may differ in details. This work considers that different news outlets are more likely to differ in their writing styles and the choice of words, and proposes a method to extract sentences based on their key information by focusing on the shared synonyms in each sentence. Our method also attempts to reduce redundancy through hierarchical clustering and arrange selected sentences on the proposed orderBERT. The results show that the proposed unsupervised framework successfully improves the coverage, coherence, and, meanwhile, reduces the redundancy for a generated summary. Moreover, due to the process of obtaining the dataset, we also propose a data refinement method to alleviate the problems of undesirable texts, which result from the process of automatic scraping.
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning
Authors: Hsiao-Wen Tseng, Chia-Hui Chang, Hsiu-Min Chuang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Multi-Task Learning, NER, Sentiment Analysis, Transfer Learning

When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and aspect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network and DANN architecture to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model; DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews
Authors: Yu-Ya Cheng, Wen-Chao Yeh, Yan-Ming Chen, Yung-Chun Chang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Emotion Recognition, Sentiment Analysis

With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user’s experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.
Numerical Relation Detection in Financial Tweets using Dependency-aware Deep Neural Network
Authors: Yu-Chi Liang, Min-Chen Chen, Wen-Chao Yeh, Yung-Chun Chang
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: Relation

Machine learning methods for financial document analysis have been focusing mainly on the textual part. However, the numerical parts of these documents are also rich in information content. In order to further analyze the financial text, we should assay the numeric information in depth. In light of this, the purpose of this research is to identify the linking between the target cashtag and the target numeral in financial tweets, which is more challenging than analyzing news and official documents. In this research, we developed a multi model fusion approach which integrates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). We also encode dependency information behind text into the model to derive semantic latent features. The experimental results show that our model can achieve remarkable performance and outperform comparisons.
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction
Authors: Jenq-Haur Wang, Kuan-Ting Chen
Date: 2021-10-01 00:00:00
Proceeding: ROCLING 2021 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentiment Analysis, Sentiment Classification, Word Embeddings

Conventional opinion polls were usually conducted via questionnaires or phone interviews, which are time-consuming and error-prone. With the advances in social networking platforms, it’s easier for the general public to express their opinions on popular topics. Given the huge amount of user opinions, it would be useful if we can automatically collect and aggregate the overall topical stance for a specific topic. In this paper, we propose to predict topical stances from social media by concept expansion, sentiment classification, and stance aggregation based on word embeddings. For concept expansion of a given topic, related posts are collected from social media and clustered by word embeddings. Then, major keywords are extracted by word segmentation and named entity recognition methods. For sentiment classification and aggregation, machine learning methods are used to train sentiment lexicon with word embeddings. Then, the sentiment scores from user-centric and post-centric views are aggregated as the total stance on the topic. In the experiments, we evaluated the performance of our proposed approach using social media data from online forums. The experimental results for 2016 Taiwan Presidential Election showed that our proposed method can effectively expand keywords and aggregate topical stances from the public for accurate prediction of election results. The best performance is 0.52% in terms of mean absolute error (MAE). Further investigation is needed to evaluate the performance of the proposed method in larger scales.