International Journal of Computational Linguistics & Chinese Language
Processing
[䏿�]
Vol. 25, No. 2, December 2020
Title:
Analyzing the Morphological Structures in Seediq Words
Author:
Chuan-Jie Lin, Li-May Sung, Jing-Sheng You, Wei Wang, Cheng-Hsun Lee, and Zih-Cyuan Liao
Abstract:
NLP
techniques are efficient to build large datasets for low-resource languages. It
is helpful for preservation and revitalization of the indigenous languages. This
paper proposes approaches to analyze morphological structures in Seediq words
automatically as the first step to develop NLP applications such as machine
translation. Word inflections in Seediq are plentiful. Sets of morphological
rules have been created according to the linguisitic features provided in the
Seediq syntax book (Sung, 2018) and based on regular morpho-phonological
processing in Seediq, a new idea of �筂eep root�� is also suggested. The
rule-based system proposed in this paper can successfully detect the existence
of infixes and suffixes in Seediq with a precision of 98.88% and a recall of
89.59%. The structure of a prefix string is predicted by probabilistic models.
We conclude that the best system is bigram model with back-off approach and
Lidstone smoothing with an accuracy of 82.86%.
Keywords: Seediq, Automatic Analysis of Morphological Structures, Deep Root, Natural Language Processing for Indigenous Languages in Taiwan, Formosan Languages
Title:
Chinese Healthcare Named Entity Recognition Based on Graph Neural Networks
Author:
Yi Lu and Lung-Hao Lee
Abstract:
Named
Entity Recognition (NER) focuses on locating the mentions of name entities and
classifying their types, usually referring to proper nouns such as persons,
places, organizations, dates, and times. The NER results can be used as the
basis for relationship extraction, event detection and tracking, knowledge graph
building, and question answering system. NER studies usually regard this
research topic as a sequence labeling problem and learns the labeling model
through the large-scale corpus. We propose a GGSNN (Gated Graph Sequence Neural
Networks) model for Chinese healthcare NER. We derive a character representation
based on multiple embeddings in different granularities from the radical,
character to word levels. An adapted gated graph sequence neural network is
involved to incorporate named entity information in the dictionaries. A standard
BiLSTM-CRF is then used to identify named entities and classify their types in
the healthcare domain. We firstly crawled articles from websites that provide
healthcare information, online health-related news and medical question/answer
forums. We then randomly selected partial sentences to retain content diversity.
It includes 30,692 sentences with a total of around 1.5 million characters or
91.7 thousand words. After manual annotation, we have 68,460 named entities
across 10 entity types: body, symptom, instrument, examination, chemical,
disease, drug, supplement, treatment, and time. Based on further experiments and
error analysis, our proposed method achieved the best F1-score of 75.69% that
outperforms previous models including the BiLSTM-CRF, Lattice, Gazetteers, and
ME-CNER. In summary, our GGSNN model is an effective and efficient solution for
the Chinese healthcare NER task.
Keywords:
Named
Entity Recognition, Graph Neural Networks, Information Extraction, Health
Informatics
Title:
Improving Word Alignment
for
Extraction Phrasal Translation
Author:
Yi-Jyun Chen, Ching-Yu Helen Yang and Jason S. Chang
Abstract:
This
thesis presents a method for extracting translations of noun-preposition
collocations from bilingual parallel corpora. The results provide researchers a
reference tool for generating grammar rules. In this paper, we use statistical
methods to extract translations of nouns and prepositions from bilingual
parallel corpora with sentence alignment, and then adjust the translations
according to the Chinese collocations extracted from a Chinese corpus. Finally,
we generate example sentences for the translations. The evaluation is done using
randomly 30 selected phrases. We used human judge to assess the translations.
Keywords:
Word
Alignment, Grammar Patterns, Collocations, Phrase Translation
Title:
NSYSU+CHT Speaker Verification System for Far-Field Speaker Verification
Challenge 2020
Author:
Yu-Jia
Zhang, Chia-Ping Chen, Shan-Wen Hsiao, Bo-Cheng Chan, and Chung-li Lu
Abstract:
In
this paper, we describe the system Team NSYSU+CHT has implemented for the 2020
Far-field Speaker Verification Challenge (FFSVC 2020). The single systems are
embedding-based neural speaker recognition systems. The front-end feature
extractor is a neural network architecture based on TDNN and CNN modules, called
TDResNet, which combines the advantages of both TDNN and CNN. In the pooling
layer, we experimented with different methods such as statistics pooling and
GhostVLAD. The back-end is a PLDA scorer. Here we evaluate PLDA
training/adaptation and use it for system fusion. We participate in the text-dependent(Task
1) and text-independent(Task 2) speaker verification tasks on single microphone
array data of FFSVC 2020. The best performance we have achieved with the
proposed methods are minDCF 0.7703, EER 9.94% on Task 1, and minDCF 0.8762, EER
10.31% on Task 2..
Keywords:
Speaker Verification, TDNN, CNN, TDResNet, GhostVLAD
Title:
A
Preliminary Study on Deep Learning-based Chinese Text to Taiwanese Speech
Synthesis System
Author:
Wen-Han Hsu, Cheng-Jung Tseng, Yuan-Fu Liao, Wern-Jun Wang and Chen-Ming Pan
Abstract:
This paper focuses on the
development and implementation of a Chinese Text-to-Taiwanese speech synthesis
system. The proposed system combines three deep neural network-based modules
including (1) a sequence-to-sequence-based Chinese characters to Taiwan Minnanyu
Luomazi Pinyin (shortened to as Tâi-lô) machine translation (called C2T from now
on), (2) a Tacotron2-based Tâi-lô pinyin to spectrogram and (3) a WaveGlow-based
spectrogram to speech waveform synthesis subsystems.
Among them, the C2T module was trained using a Chinese-Taiwanese parallel corpus (iCorpus) and 9 dictionaries released by Academia Sinica and collected from internet, respectively. The Tacotron2 and Waveglow was tuned using a Taiwanese speech synthesis corpus (a female speaker, about 10 hours speech) recorded by Chunghwa Telecom Laboratories. At the same time, a demonstration Chinese Text-to-Taiwanese speech synthesis web page has also been implemented.
From the experimental results, it was found that (1) the best syllable error rate (SER) of 6.53% was achieved by the C2T module, (2) and the average MOS score of the whole speech synthesis system evaluated by 20 listeners gains 4.30. These results confirm that the effectiveness of integration of C2T, Tacrtron2 and WaveGlow models. In addition, the real-time factor of the whole system achieved 1/3.5.Keywords:
Machine Translation, Taiwanese Speech Synthesis, Tacotron2, Waveglow
Title:
The
Preliminary Study of Robust Speech Feature Extraction based on Maximizing the
Accuracy of States in Deep Acoustic Models
Author:
Li-Chia
Chang and Jeih-weih Hung
Abstract:
In this study, we focus
on developing a novel speech feature extraction technique to achieve
noise-robust speech recognition, which employs the information from the backend
acoustic models. Without further retraining and adapting the backend acoustic
models, we use deep neural networks to learn the front-end acoustic speech
feature representation that can achieve the maximum state accuracy obtained from
the original acoustic models. Compared with the robustness methods that retrain
or adapt acoustic models, the presented method exhibits the advantages of lower
computational complexity and faster training.
Keywords:
Noise-robust Speech Feature, Speech Recognition, Deep Learning