System description	In this section, we present the system architecture. It includes how we pre-process text, what types of text representation we use and models used in our system. In the end, we combine model results by using an ensemble technique.

Embedding size is 200. comment_tokenize: CBOW embedding training in all dataset comment, each word is splited by space. Before split by space, word is concatenated by using , , . Embedding size is 200. roberta: sentence embedding training in all dataset comment, training by using RoBERTa architecture. Embedding size is 256. fasttext, sonvx* is all pre-trained word embedding in general domain. Before mapping word to vector, word is concatenated by using , , . Embedding size of fasttext is 300. (sonvx_wiki, sonvx_baomoi_w2, sonvx_baomoi_w5) have embedding size corresponding is (400, 300, 400). In our experiment, the dataset is split into two-part: train set and dev set with the corresponding ratio $(0.9, 0.1)$. Two subsets have the same imbalance ratio like the root set. For each combination of model and word embedding, we train model in train set until it achieve the best result of loss score in the dev set. The table TABREF12 shows the best result of each combination on the f1_macro score.

System description ::: Data pre-processing	Content in the dataset that provided in this HSD task is very diverse. Words having the same meaning were written in various types (teen code, non tone, emojis,..) depending on the style of users. Dataset was crawled from various sources with multiple text encodes. In order to make it easy for training, all types of encoding need to be unified. This cleaning module will be used in two processes: cleaning data before training and cleaning input in inferring phase. Following is the data processing steps that we use: Step 1: Format encoding. Vietnamese has many accents, intonations with different Unicode typing programs which may have different outputs with the same typing type. To make it unified, we build a library named visen. For example, the input "thíêt kê will be normalized to "thiết kế" as the output. Step 2: In social networks, people show their feelings a lot by emojis. Emoticon is often a special Unicode character, but sometimes, it is combined by multiple normal characters like `: ( = ]'.

We make a dictionary mapping this emoji (combined by some characters) to a single Unicode character like other emojis to make it unified. Step 3: Remove unseen characters. For human, unseen character is invisible but for a computer, it makes the model harder to process and inserts space between words, punctuation and emoji. This step aims at reducing the number of words in the dictionary which is important task, especially with low dataset resources like this HSD task. Step 4: With model requiring Vietnamese word segmentation as the input, we use ,  to tokenize the input text. Step 5: Make all string lower. We experimented and found that lower-case or upper-case are not a significant impact on the result, but with lower characters, the number of words in the dictionary is reduced. RoBERTa proposed in  an optimized method for pretraining self-supervised NLP systems. In our system, we use RoBERTa not only to make sentence representation but also to augment data. With mask mechanism, we replace a word in the input sentence with another word that RoBERTa model proposes. To reduce the impact of replacement word, the chosen words are all common words that appear in almost three classes of the dataset.

Experiment	The dataset in this HSD task is really imbalance. Clean class dominates with 91.5%, offensive class takes 5% and the rest belongs to hate class with 3.5%. To make model being able to learn with this imbalance data, we inject class weight to the loss function with the corresponding ratio (clean, offensive, hate) is $(0.09, 0.95, 0.96)$. Formular DISPLAY_FORM17 is the loss function apply for all models in our system. $w_i$ is the class weight, $y_i$ is the ground truth and $\hat{y}_i$ is the output of the model. If the class weight is not set, we find that model cannot adjust parameters. The model tends to output all clean classes. We experiment 8 types of embedding in total: comment: CBOW embedding training in all dataset comment, each word is splited by space. Embedding size is 200. comment_bpe: CBOW embedding training in all dataset comment, each word is splited by subword bpe.

For each model having the best fit on the dev set, we export the probability distribution of classes for each sample in the dev set. In this case, we only use the result of model that has f1_macro score that larger than 0.67. The probability distribution of classes is then used as feature to input into a dense model with only one hidden layer (size 128). The training process of the ensemble model is done on samples of the dev set. The best fit result is 0.7356. The final result submitted in public leaderboard is 0.73019 and in private leaderboard is 0.58455. It is quite different in bad way. That maybe is the result of the model too overfit on train set tuning on public test set. Statistics of the final result on the dev set shows that almost cases have wrong prediction from offensive and hate class to clean class belong to samples containing the word `vl'. (62% in the offensive class and 48% in the hate class). It means that model overfit the word `vl' to the clean class. This makes sense because `vl' appears too much in the clean class dataset.

System description ::: Models architecture	Social comment dataset has high variety, the core idea is using multiple model architectures to handle data in many viewpoints. In our system, we use five different model architectures combining many types of CNN, and RNN. Each model will use some types of word embedding or handle directly sentence embedding to achieve the best general result. Source code of five models is extended from the GitHub repository The first model is TextCNN (figure FIGREF2) proposed in . It only contains CNN blocks following by some Dense layers. The output of multiple CNN blocks with different kernel sizes is connected to each other. The second model is VDCNN (figure FIGREF5) inspired by the research in . Like the TextCNN model, it contains multiple CNN blocks. The addition in this model is its residual connection. The third model is a simple LSTM bidirectional model (figure FIGREF15). It contains multiple LSTM bidirectional blocks stacked to each other. The fourth model is LSTMCNN (figure FIGREF24). Before going through CNN blocks, series of word embedding will be transformed by LSTM bidirectional block. The final model is the system named SARNN (figure FIGREF25). It adds an attention block between LTSM blocks.

With the multiply output results, we will use an ensemble method to combine them and output the final result. Ensemble method we use here is Stacking method will be introduced in the section SECREF16.

System description ::: System overview	The fundamental idea of this system is how to make a system that has the diversity of viewing an input. That because of the variety of the meaning in Vietnamese language especially with the acronym, teen code type. To make this diversity, after cleaning raw text input, we use multiple types of word tokenizers. Each one of these tokenizers, we combine with some types of representation methods, including word to vector methods such as continuous bag of words , pre-trained embedding as fasttext (trained on Wiki Vietnamese language)  and sonvx (trained on Vietnamese newspaper) . Each sentence has a set of words corresponding to a set of word vectors, and that set of word vectors is a representation of a sentence. We also make a sentence embedding by using RoBERTa architecture . CBOW and RoBERTa models trained on text from some resources including VLSP 2016 Sentiment Analysis, VLSP 2018 Sentiment Analysis, VLSP 2019 HSD and text crawled from Facebook. After having sentence representation, we use some classification models to classify input sentences. Those models will be described in detail in the section SECREF13.

VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination	Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking different opinions. To deal with this problem, SNSs have to use a lot of resources including people to clean the aforementioned content. In this paper, we propose a supervised learning model based on the ensemble method to solve the problem of detecting hate content on SNSs in order to make conversations on SNSs more effective. Our proposed model got the first place for public dashboard with 0.730 F1 macro-score and the third place with 0.584 F1 macro-score for private dashboard at the sixth international workshop on Vietnamese Language and Speech Processing 2019.

System description ::: Ensemble method	Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model. Have the main three types of ensemble methods including Bagging, Boosting and Stacking. In this system, we use the Stacking method. In this method, the output of each model is not only class id but also the probability of each class in the set of three classes. This probability will become a feature for the ensemble model. The stacking ensemble model here is a simple full-connection model with input is all of probability that output from sub-model. The output is the probability of each class.

This HSD task is much more difficult when it requires classify text in three classes, with hate and offensive class quite hard to classify even with humans. In this paper, we propose a method to handle this HSD problem. Our system combines multiple text representations and models architecture in order to make diverse predictions. The system is heavily based on the ensemble method. The next section will present detail of our system including data preparation (how we clean text and build text representation), architecture of the model using in the system, and how we combine them together. The third section is our experiment and result report in HSD shared-task VLSP 2019. The final section is our conclusion with advantages and disadvantages of the system following by our perspective.

Hate speech (hate): an item is identified as hate speech if it (1) targets individuals or groups on the basis of their characteristics; (2) demonstrates a clear intention to incite harm, or to promote hatred; (3) may or may not use offensive or profane words. Offensive but not hate speech (offensive): an item (posts/comments) may contain offensive words but it does not target individuals or groups on the basis of their characteristics. Neither offensive nor hate speech (clean): normal item, it does not contain offensive language or hate speech. The term `hate speech' was formally defined as `any communication that disparages a person or a group on the basis of some characteristics (to be referred to as types of hate or hate classes) such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics' . Many researches have been conducted in recent years to develop automatic methods for hate speech detection in the social media domain. These typically employ semantic content analysis techniques built on Natural Language Processing (NLP) and Machine Learning (ML) methods. The task typically involves classifying textual content into non-hate or hateful.

Conclusion	In this study, we experiment the combination of multiple embedding types and multiple model architecture to solve a part of the problem Hate Speech Detection with a signification good classification results. Our system heavily based on the ensemble technique so the weakness of the system is slow processing speed. But in fact, it is not big trouble with this HSD problem when human usually involve handling directly in the before. HSD is a hard problem even with human. In order to improve classification quality, in the future, we need to collect more data especially social networks content. This will make building text representation more correct and help model easier to classify. british

In case the model predicts wrong from the clean class to the offensive class and the hate class, the model tends to decide case having sensitive words to be wrong class. The class offensive and the hate are quite difficult to distinguish even with human.

For example, with input `nhổn làm gắt vl', we can augment to other outputs: `vl làm gắt qá', `còn làm vl vậy', `vl làm đỉnh vl' or `thanh chút gắt vl'. british

Introduction	Currently, social networks are so popular. Some of the biggest ones include Facebook, Twitter, Youtube,... with extremely number of users. Thus, controlling content of those platforms is essential. For years, social media companies such as Twitter, Facebook, and YouTube have been investing hundreds of millions euros on this task , . However, their effort is not enough since such efforts are primarily based on manual moderation to identify and delete offensive materials. The process is labour intensive, time consuming, and not sustainable or scalable in reality , , . In the sixth international workshop on Vietnamese Language and Speech Processing (VLSP 2019), the Hate Speech Detection (HSD) task is proposed as one of the shared-tasks to handle the problem related to controlling content in SNSs. HSD is required to build a multi-class classification model that is capable of classifying an item to one of 3 classes (hate, offensive, clean).

Question: What dataset do they use?
Answer: They used Wiki Vietnamese language and Vietnamese newspapers to pretrain embeddings and dataset provided in HSD task to train model (details not mentioned in paper).