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Found 37 papers, 2 papers with code
Syllable based DNN-HMM Cantonese Speech to Text System
This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model.
Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation
To ascertain the effectiveness of our approach, our SANet model achieved competitive results on the real-time CamVid and cityscape datasets.
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models
(2) Conversely, using an encoder to warm-start seq2seq training, we show that by unfreezing the encoder partway through training, we can match task performance of a from-scratch seq2seq model.
Weighted Ensembles for Active Learning with Adaptivity
Labeled data can be expensive to acquire in several application domains, including medical imaging, robotics, and computer vision.
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Tests on synthetic functions and real-world applications showcase the merits of the proposed method.
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes
Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous.
Incremental Ensemble Gaussian Processes
Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based approaches have well-documented merits not only in learning over a rich class of nonlinear functions, but also in quantifying the associated uncertainty.
PolyU CBS-Comp at SemEval-2021 Task 1: Lexical Complexity Prediction (LCP)
In this contribution, we describe the system presented by the PolyU CBS-Comp Team at the Task 1 of SemEval 2021, where the goal was the estimation of the complexity of words in a given sentence context.
Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource
Mandarin Alphabetical Word (MAW) is one indispensable component of Modern Chinese that demonstrates unique code-mixing idiosyncrasies influenced by language exchanges.
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings
Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one.
Affection Driven Neural Networks for Sentiment Analysis
Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade.
Ciron: a New Benchmark Dataset for Chinese Irony Detection
Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research.
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge
Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks.
Ranked #2 on
Emotion Classification
on SemEval 2018 Task 1E-c
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis
Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model.
Dual Memory Network Model for Biased Product Review Classification
In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks.
Ranked #6 on
Sentiment Analysis
on User and product information
Fake News Detection Through Multi-Perspective Speaker Profiles
This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection.
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases.
A Cognition Based Attention Model for Sentiment Analysis
Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly.
A Question Answering Approach for Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
A Question Answering Approach to Emotion Cause Extraction
Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text.
Ranked #8 on
Emotion Cause Extraction
on ECE
Leveraging Eventive Information for Better Metaphor Detection and Classification
We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups.
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words.
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
When the classification is binary, ROOT9 achieves the following results against the baseline: hypernyms-co-hyponyms 95. 7% vs. 69. 8%, hypernyms-random 91. 8% vs. 64. 1% and co-hyponyms-random 97. 8% vs. 79. 4%.
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists.
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words.
A Grammar-informed Corpus-based Sentence Database for Linguistic and Computational Studies
We adopt the corpus-informed approach to example sentence selections for the construction of a reference grammar.
