{
"paper_id": "2020",
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"date_generated": "2023-01-19T12:13:57.773056Z"
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"title": "Applying Graph Neural Networks for Vietnamese Dependency Parsing",
"authors": [
{
"first": "Nguyen",
"middle": [],
"last": "Duc",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Thien",
"middle": [],
"last": "Nguyen",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Thi",
"middle": [
"Thu"
],
"last": "Trang",
"suffix": "",
"affiliation": {},
"email": "trangntt@soict.hust.edu.vn"
},
{
"first": "Truong",
"middle": [],
"last": "Dang Quan\u01f5",
"suffix": "",
"affiliation": {},
"email": "dangquangtruong98@gmail.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper presents a state-of-the-art model to solve the Vietnamese dependency parsing task (HA My Linh, 2020) in VLSP 2020 1 Evaluation Campaign. In this model, the Bidirectional Long Short-Term Memory (BiLSTM) network is used to extract the contextual information, while the graph neural network captures high-order information. Some preprocessing for Vietnamese raw texts are included for the training, such as word segmentation, part-of-speech (POS) tagging for the model. We modified the network with suitable word embedding mechanisms, i.e., fastText, to represent the semantic information of words more accurately. Therefore, Vietnamese words that are marked as unknown tokens now can have the right embedding; thus, they will be well modeled in dependency parsing. Experiments on the raw text dataset show that the model achieved an average of 72.85% of unlabeled attachment score (UAS) and 64.35% of labeled attachment score (LAS). With the Segmentation and POS tagging dataset, we achieved a higher average of 81.71% (UAS) and 73.19% (LAS).",
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"abstract": [
{
"text": "This paper presents a state-of-the-art model to solve the Vietnamese dependency parsing task (HA My Linh, 2020) in VLSP 2020 1 Evaluation Campaign. In this model, the Bidirectional Long Short-Term Memory (BiLSTM) network is used to extract the contextual information, while the graph neural network captures high-order information. Some preprocessing for Vietnamese raw texts are included for the training, such as word segmentation, part-of-speech (POS) tagging for the model. We modified the network with suitable word embedding mechanisms, i.e., fastText, to represent the semantic information of words more accurately. Therefore, Vietnamese words that are marked as unknown tokens now can have the right embedding; thus, they will be well modeled in dependency parsing. Experiments on the raw text dataset show that the model achieved an average of 72.85% of unlabeled attachment score (UAS) and 64.35% of labeled attachment score (LAS). With the Segmentation and POS tagging dataset, we achieved a higher average of 81.71% (UAS) and 73.19% (LAS).",
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"section": "Abstract",
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"text": "In recent years, dependency parsing is a fascinating research topic and has a large number of applications in natural language processing. This task is to automatically identify the relationship between words in a sentence and label the relationship between the head and the dependency word, and thus, establish the grammatical structure of the sentence. Traditional graph-based dependency parsing only extracts the parent-child relationship and ignores deeper relationships. Hence, we decided to experiment with the idea of extracting deeper relation-ships of the neighbor nodes, which is extensively covered in the paper Ji et al. (2019) .",
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"start": 623,
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"text": "Ji et al. (2019)",
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"section": "Introduction",
"sec_num": "1"
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"text": "This state-of-the-art model achieved good performance due to its ability to represent incorrect Out-Of-Vocabulary (OOV) words in the input layer for Vietnamese. Normally, words that are not found in the vocabulary will be marked as unknown tokens before feeding to the embedding layer. This caused the model to embed OOV words incorrectly; therefore, it created the loss of information in calculating attention distribution. In this paper, we modified the pre-trained layer of word embedding for the graph neural networks with a more suitable embedding mechanism for Vietnamese, which solved the issue well.",
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"section": "Introduction",
"sec_num": "1"
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"text": "The rest of this paper is organized as follows: Section 2 presents the architecture and its components of graph neural networks. The experiments are shown in Section 3. Finally, Section 4 concludes the paper and gives some perspectives for the work.",
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"section": "Introduction",
"sec_num": "1"
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"text": "Normally, Graph-based dependency parser search through the space of possible trees for a given sentence encoded as directed graphs and use methods from graph theory (Maximum Spanning Tree or greedy algorithm) for the optimal solutions. However, in the Graph Neural Network (GNN) model, the dependency parser utilizes the neural network to assign a weight to each edge, then construct a MST from the edge weight (Dozat et al., 2017) . For maximum accuracy, we need to analyze the surface form and the deep structure of the graph. There are three main components in the model: Encoder extracts the surface form and the contextual information and turns them into the nodes (words) representations for the next components; The graph attention network (a subset of GNN, using the structure from Veli\u010dkovi\u0107 et al. (2017) ) layers then extract the deep structure and high-order information to illustrate the head-dependent relationships of the nodes; the final component is the decoder, used to create the dependency tree from the output of the GNN. We will discuss the details in the following sections.",
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"text": "(Dozat et al., 2017)",
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"text": "Veli\u010dkovi\u0107 et al. (2017)",
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"section": "Methodology",
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"text": "First, we used the VNCoreNLP -suggested by Vu et al. (2018) -to segment and perform the POS tagging on the raw text. VNCoreNLP used a transformation rule-based learning model for the segmentation of the Vietnamese document, thus, obtained faster and better accuracy than all previous segmentation tools, as the model accounted for the fact that Vietnamese words are created from syllables including the space character (Nguyen et al., 2017) . The VNCoreNLP performed the task of labeling words with POS tag Vu et al. (2018) via MarMot (a CRF framework), state of the art POS and morphological tagger (M\u00fcller et al., 2013) Word embedding is the most popular representation method for words in a document because it captures the context of words, semantic and syntactic similarity, relation with other words, etc. Using word embedding makes it easier to represent words with less memory than using a one-hot vector while also showing the relationship between words.",
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"text": "With a huge training corpus (e.g., a total of 100 billion words with a 3-million-word vocab in Google News), the pre-trained model can cover much more context for word embedding than the auto-updating mechanism of the word embedding in the end-to-end abstractive summarization model with its training corpus (e.g., a total of 240 million words with a 50k-word vocab in Daily Mail/CNN) (Anh and Trang, 2019).",
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"text": "In this paper, we adopted a suitable pre-trained model for Vietnamese with 300-dimensional word embeddings, i.e., fastText from Facebook , for the word embedding layer. The fastText trained on the Wikipedia dataset with character n-grams of length 5 by CBOW 2 method. fast-Text is more suitable in our case as when the GNN model meets unknown vocab, the fastText generates an embedding of the vocab with value 0, resulting in error reductions; meanwhile, the Word2Vec and the GloVe does not do that. This method enables fastText to handle OOV 3 words by constructing the vector for OOV words from its characters. Both GloVe and Word2Vec are unable to do so.",
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"text": "According to Kiperwasser and Goldberg (2016) , we can apply BiLSTM model to create the dependency tree as illustrated in Figure [ 1] Firstly, each word is embedded using a vector combined from three different vectors: randomly initialized word embedding, pre-trained word embedding, and partof-speech embedding.",
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"raw_str": "x i = e(w i ) \u2295 e (w i ) \u2295 e(pos i )",
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"text": "As a result, the x i illustrated the sentence of the word i in [2] . Given the position i of the word, the BiLSTM model can compute state vectors \u2212 \u2192 c i and \u2190 \u2212 c i where the \u2212 \u2192 c i is draw from the start of the sentence to the position i and \u2190 \u2212 c i is from the end of the sentence to i.",
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"text": "EQUATION",
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"raw_str": "\u2212 \u2192 c i = \u2212\u2212\u2212\u2212\u2212\u2212\u2212\u2192 LST M (x i ) \u2295 \u2190\u2212\u2212\u2212\u2212\u2212\u2212\u2212 LST M (x i )",
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"sec_num": "2.2"
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"text": "The two vectors \u2212 \u2192 c i and \u2190 \u2212 c i then concatenate to become the context-dependent representation of the word i. Thus we can use multilayers perceptron (MLP) to define two-node representations of the word i the probability of being the head role vector and probability of being the dependent role vector (Dozat et al., 2017) :",
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"text": "EQUATION",
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"raw_str": "h i = M LP h (c i ), d i = M LP d (c i )",
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"text": "The score function is a SoftMax function, where the representations of the word i and j is the input, therefore complementing the analysis of the surface form of the segmented sentence. As a result, the output of the BiLSTM component is a complete weight graph model. (Dozat et al., 2017) ",
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"text": "\u03c3(i, j) = Softmax i (h T j Ad j + b T 1 h j + b T 2 h j ) (4)",
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"text": "In the implementation, the GNN component can utilize at most three layers, each layer consists of 4 graph neural network units as illustrated in Figure [1] -where the representation of the vectors is calculated from the same representation in the previous layer using this formula where g is the LeakyReLU function, t is the layer, v i is the vector representation of i, and a ij is the edge weight of ",
"cite_spans": [],
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{
"start": 145,
"end": 155,
"text": "Figure [1]",
"ref_id": "FIGREF0"
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"sec_num": "2.3"
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{
"text": "EQUATION",
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"ref_id": "EQREF",
"raw_str": "v t i = g \uf8eb \uf8ed W j\u2208N (i) \u03b1 t ij v t\u22121 j + Bv t\u22121 i \uf8f6 \uf8f8",
"eq_num": "(5)"
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"sec_num": "2.3"
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"text": "We can apply the formula [5] to analyze the high order information of the nodes which is represented in three ways: grandparents, grandchildren, and siblings ( Figure [2] ) (Eisner, 1997) .",
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"start": 173,
"end": 187,
"text": "(Eisner, 1997)",
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"start": 160,
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"text": "Figure [2]",
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"sec_num": "2.3"
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"text": "Specifically, the head representation of node i should attend to the neighbors' representation as they are the parents of the i. Therefore the model can calculate h i from the h j of the previous layer t \u2212 1 using the formula [5]:",
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{
"text": "EQUATION",
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"start": 0,
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\uf8f1 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f3 h t i = g W 1 j\u2208N (i) \u03b1 t ji h t\u22121 j + B 1 h t\u22121 i d t i = g W 2 j\u2208N (i) \u03b1 t ij d t\u22121 j + B 2 d t\u22121 i",
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"sec_num": "2.3"
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"text": "6) The dependent node d i 's computation operation is the same as the head node's one i. Thus the equation [6] can assist to analyse the order of the relationship of grandparents and grandchild.",
"cite_spans": [],
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"section": "GNN Layers",
"sec_num": "2.3"
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"text": "To examine the sibling relationships, the head representation of the node i check the neighborhood where they are dependent on node i. Thus the formula will update the h i in the following way: and siblings.",
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"section": "GNN Layers",
"sec_num": "2.3"
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{
"text": "EQUATION",
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"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\uf8f1 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f3 h t i = g W 1 j\u2208N (i) \u03b1 t ji d t\u22121 j + B 1 h t\u22121 i d t i = g W 2 j\u2208N (i) \u03b1 t ij h t\u22121 j + B 2 d t\u22121 i",
"eq_num": "(7)"
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"sec_num": "2.3"
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{
"text": "EQUATION",
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{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\uf8f1 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f3 h t i = g(W 1 j\u2208N (i) (\u03b1 t ji h t\u22121 j + \u03b1 t ji d t\u22121 j ) +B 1 h t\u22121 i ) d t i = g(W 2 j\u2208N (i) (\u03b1 t ij h t\u22121 i + \u03b1 t ji d t\u22121 j ) +B 2 d t\u22121 i )",
"eq_num": "(8)"
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],
"section": "GNN Layers",
"sec_num": "2.3"
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"text": "As the equations [8] illustrated, the edge weight a ij is the decisive element responsible for the update of the relationship information. The edge weight is figured with the following formula:",
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"sec_num": "2.3"
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{
"text": "EQUATION",
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{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u03b1 t ij = \uf8f1 \uf8f2 \uf8f3 Softmax i (h T i Ad j + b T 1 h i + b T 2 d j ) i \u2208 N t k (j) 0, otherwise",
"eq_num": "(9)"
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],
"section": "GNN Layers",
"sec_num": "2.3"
},
{
"text": "After the high-order information is extracted from the GNN and enhanced the nodes representations, the node representation will be used to build the dependency tree via Biaffine parser (the setting is identical to Dozat et al. (2017)) 3 Experiments",
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{
"start": 214,
"end": 234,
"text": "Dozat et al. (2017))",
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"section": "Decoder",
"sec_num": "2.4"
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"text": "The VLSP provided the datasets and separated them into training datasets and raw text datasets. The data for training was further divided into two packages: the first package consists of 5070 sentences, with a large domain from the social media comments on restaurants and hotels (100 sentences), to the story of the Little Prince (1570 sentences) and the VietTreeBank -VTB (3400 sentences); the second package includes 3000 sentences with diverse origins.",
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"section": "Dataset",
"sec_num": "3.1"
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"text": "The raw text data for prediction includes the two packages above, and 20 raw text files crawled from VnExpress news articles. The VTB files and the files with index 1,3,4,7,8,10,14 were accurately tokenized and labeled.",
"cite_spans": [],
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"section": "Dataset",
"sec_num": "3.1"
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"text": "The graph-based dependency parsing neural network model has one important characteristic: the raw text dataset's sentences have to be tokenized for the training to be carried out successfully. Therefore the VNCoreNLP -an NLP pipeline used for POS tagging, named entity recognition and dependency parsing is useful here in this case [4] . This tool is capable of providing highly accurate annotation for the input sentences, therefore improving the score of the training model.",
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"section": "Dataset",
"sec_num": "3.1"
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"text": "The training operation consists of two methods: First, we have to decode the output of the final layer of the GNN component (denoted by)",
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"section": "Training",
"sec_num": "3.2"
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{
"text": "EQUATION",
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{
"start": 0,
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u03b1 t ij = \u03c3 t (i, j) = P t (i|j)",
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"section": "Training",
"sec_num": "3.2"
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"text": "which are the tree structures (computed by P (i|j)) and the dependency edge labels (measured by P (r|i, j), which indicated the probability a tree (i, j) holds a dependency relation r, using another MLP from biaffine parser (Dozat et al., 2017) , the loss function of the classifier is computed with the equation:",
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{
"start": 224,
"end": 244,
"text": "(Dozat et al., 2017)",
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"sec_num": "3.2"
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{
"text": "EQUATION",
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"start": 0,
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"ref_id": "EQREF",
"raw_str": "L 0 = \u2212 1 n (i,j,r)\u2208T (logP \u03c4 (i|j) + logP (r|i, j))",
"eq_num": "(11)"
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"sec_num": "3.2"
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"text": "Second, the model can supervise on P t (i|j) from each layer of the GNN component, therefore the layer-wise loss will be computed with the equation:",
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"section": "Training",
"sec_num": "3.2"
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{
"text": "EQUATION",
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"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L = \u03c4 t=1 L t = \u03c4 t=1 \u2212 1 n (i,j,r)\u2208T logP (r|i, j)",
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],
"section": "Training",
"sec_num": "3.2"
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{
"text": "The main objective is to minimize the loss of combination of them:",
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"sec_num": "3.2"
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{
"text": "EQUATION",
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{
"start": 0,
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L = \u03bb 1 L 0 + \u03bb 2 L",
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],
"section": "Training",
"sec_num": "3.2"
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{
"text": "We have implemented and operated the model on the AWS Server (AWS Deep Learning AMI (Ubuntu 18.04) Version 34.0 installed in the EC2 Instance p3.2xlarge -GPU NVIDIA Tesla v100 16 GB, Memory 61 GB, SSD 100 GB, CPU 8 Virtual Cores) successfully. The hyperparameters configuration in Table [ 1] has slight modifications. For the word embedding, we used fastText with Vietnamese data as the primary pre-trained model, which has 300 dimensions instead of 100 dimensions of GloVe that Ji et al. (2019) used. Then, we concatenate the pre-trained word embedding with 200-dimension randomly initialize word embedding and 100-dimension part-ofspeech embedding. Randomly embedding vectors obtained from binomial distribution. The training operation took approximately one hour.",
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"start": 479,
"end": 495,
"text": "Ji et al. (2019)",
"ref_id": "BIBREF5"
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"start": 281,
"end": 288,
"text": "Table [",
"ref_id": "TABREF5"
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"section": "Results",
"sec_num": "3.3"
},
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"text": "The main evaluators for the dependency parsing problem are LAS and UAS. The results are coming from the script evaluator 2018. For the labeled data, the highest UAS is 81.89% from the VTB package, meanwhile the package Test VNExpress 14 achieved the highest LAS 73.57%. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "3.3"
},
{
"text": "To conclude, our experiment on using the graph neural network for graph-based dependency parsing suggests that understanding the deep structure of the representations of words via nodes' message passing improved a slightly better accuracy and efficiency than other traditional graph-based dependency parsers. In future works, we are planning to improve the performance of the model by applying Conditional Random Fields in the labeling process for the nodes before extracting the high-order information via graph neural network.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "4"
},
{
"text": "Continuous Bag of Words 3 Out-of-vocabulary",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "The authors wish to thank VLSP organizers for their reviews and encouragement.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
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