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"title": "Overview of VLSP RelEx shared task: A Data Challenge for Semantic Relation Extraction from Vietnamese News",
"authors": [
{
"first": "Mai-Vu",
"middle": [],
"last": "Tran",
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"institution": "VNU University of Engineering and Technology",
"location": {
"settlement": "Hanoi",
"country": "Vietnam"
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{
"first": "Hoang-Quynh",
"middle": [],
"last": "Le",
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"institution": "VNU University of Engineering and Technology",
"location": {
"settlement": "Hanoi",
"country": "Vietnam"
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{
"first": "Duy-Cat",
"middle": [],
"last": "Can",
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"institution": "VNU University of Engineering and Technology",
"location": {
"settlement": "Hanoi",
"country": "Vietnam"
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{
"first": "Huyen",
"middle": [],
"last": "Nguyen",
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"affiliation": {
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"institution": "Hanoi University of Science",
"location": {
"country": "Vietnam"
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"email": "huyenntm@hus.edu.vn"
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{
"first": "Linh",
"middle": [],
"last": "Nguyen",
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"email": "linhntn3@viettel.com.vn"
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{
"first": "Tran",
"middle": [],
"last": "Ngoc",
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{
"first": "Tam",
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"last": "Doan",
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"abstract": "This paper reports the overview of RelEx shared task for semantic relation extraction from Vietnamese News, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). This task focuses on classifying entity pairs in Vietnamese News text into four different, non-overlapping categories of semantic relations defined in advance. In order to generate a fair benchmark, we build a humanannotated dataset of 1,056 documents and 5,900 instances of semantic relations, collected from Vietnamese News in several domains. All models will be evaluated in terms of macro-and micro-averaged F1 scores, two typical evaluation metrics for semantic relation extraction problem.",
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"text": "This paper reports the overview of RelEx shared task for semantic relation extraction from Vietnamese News, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). This task focuses on classifying entity pairs in Vietnamese News text into four different, non-overlapping categories of semantic relations defined in advance. In order to generate a fair benchmark, we build a humanannotated dataset of 1,056 documents and 5,900 instances of semantic relations, collected from Vietnamese News in several domains. All models will be evaluated in terms of macro-and micro-averaged F1 scores, two typical evaluation metrics for semantic relation extraction problem.",
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"text": "The rapid growth of volume and variety of news brings an unprecedented opportunity to explore electronic text but an enormous challenge when facing a massive amount of unstructured and semistructured data. Recent research progress in text mining needs to be supported by Information Extraction (IE) and Natural Language Processing (NLP) techniques. One of the most fundamental sub-tasks of IE is Relation Extraction (RE). It is the task of identifying and determining the semantic relations between pairs of named entity mentions (or nominals) in the text (Aggarwal, 2015) . Receiving the (set of) document(s) as an input, the relation extraction system aims to extract all predefined relationships mentioned in this document by identifying the corresponding entities and determining the type of relationship between each pair of entities (see examples in Figure 1 ). RE is of significant importance to many fields and applications, ranging from ontology building (Thukral et al., 2018) , improving the access to scientific literature (G\u00e1bor et al., 2018) , question answering (Lukovnikov et al., 2017; Das et al., 2017) to major life events extraction (Li et al., 2014; Cavalin et al., 2016 ) and many other applications. However, manually curating relations is plagued by its high cost and the rapid growth of the electronic text.",
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"text": "(Aggarwal, 2015)",
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"text": "(Thukral et al., 2018)",
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"text": "Figure 1",
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"section": "Introduction",
"sec_num": "1"
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"text": "For English, several challenge evaluations have been organized such as Semantic Evaluation (Se-mEval) (G\u00e1bor et al., 2018; Hendrickx et al., 2010) , BioNLP shared task (Del\u00e9ger et al., 2016) , and Automatic Content Extraction (ACE) (Walker et al., 2006) . These challenges evaluations attracted many scientists worldwide to attend and publish their latest research on semantic relation extraction. Many approaches are proposed for RE in English texts, ranging from knowledgebased methods to machine learning-based methods (Bach and Badaskar, 2007; Dongmei et al., 2020) . Studies on this problem for Vietnamese text are still in the early stages with a few initial achievements. In recent years, there has been a growing interest to develop computational ap-proaches for extracting semantic relations in Vietnamese text automatically with proposals of several methods. Despite these attempts, the lack of a comprehensive benchmarking dataset has limited the comparison of different techniques. RelEx challenge task in VLSP was set up to provide an opportunity for researchers to propose, assess and advance their researches.",
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"text": "Hendrickx et al., 2010)",
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"section": "Introduction",
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"text": "The remainder of the paper is organized as follows. Section 2 gives the description about RelEx shared task. The next section describes the data collection and annotation methodologies. Subsequently, section 4 describes the competition, approaches and respective results. Finally, Section 5 concludes the paper.",
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"section": "Introduction",
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"text": "As the first shared task of relation extraction for Vietnamese text, we go from typical relations between three fundamental entities in News domain: Location, Organization and P erson. All semantic relations between nominals other than the aforementioned entities were excluded. Based on these three types of annotated entities, we selected four relation types with coverage sufficiently broad to be of general and practical interest. Our selection is referenced and modified based on the relation types and subtypes used in the ACE 2005 task (Walker et al., 2006) . We aimed at avoiding semantic overlap as much as possible. Four relation types are described in Table 1 and as follow.",
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"text": "\u2022 The LOCAT ED relation captures the physical or geographical location of an entity.",
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"text": "\u2022 The P ART \u2212 W HOLE relation type captures the relationship when the parts contribute to the structure of the wholes.",
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"text": "\u2022 The P ERSON AL\u2212SOCIAL relations describe the relationship between people.",
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"text": "\u2022 The ORGAN IZAT ION \u2212 AF F ILIAT ION relation type represents the organizational relationship of entities.",
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"text": "\u2022 We do not annotate non-relation entity pairs (N ON E). These negatives instances need to be self-generated by participated teams, if necessary.",
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"text": "In the case of P ERSON AL \u2212 SOCIAL, an undirected relation type, two entities are symmetric (i.e., not ordered). Other relation types are directed, i.e., their entities are asymmetry (i.e., order sensitive). We restrict the direction of these relation types always come from entity 1 to entity 2. The participated system needs to define which entity mention plays the role of entity 1 and which entity mention plays the role of entity 2.",
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"text": "This task only focused on intra-sentence relation extraction, i.e., we limit relations to only those that are expressed within a single sentence. The relations between entity mentions are annotated if and only if the relationship is explicitly referenced in the sentence that contains the two mentions. Even if there is a relationship between two entities in the real world (or elsewhere in the document), there must be evidence for that relationship in the local context where it is tagged. We do not accept the case of bridging relations (i.e., a relationship derived from two other consecutive relationships), uncertain relations, inferred relations, and relation in the future tenser (i.e., allusion/mean to happen in the future).",
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"text": "A relation is defined by two entities participating in this relationship. In other words, a sentence can contain several different relations if it has more than one pairs of entities. Any qualifying relations must be predicted, even if the text mentions them is overlap or nested with range text of other relations. We do not allow the multi-label cases, i.e., a pair of entities must have only one relationship or no relation. If there is an ambiguity between some relation types, the participated system needs to decide to choose the most suitable label.",
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"text": "Only binary relations are accepted. N-nary relations should be predicted if and only if they can be split into several binary relations without changing the semantic meaning of the relationships.",
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"text": "For the task, we prepared a total of 1, 056 News documents: 506 documents for the training, 250 documents for development and 300 documents in the test set. Of all 1, 056 news documents, 815 documents were selected in a single crawler process. The remaining 241 documents were selected in another crawler process to represent difference features and were incorporated into the test set. We",
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"section": "Data Statistics",
"sec_num": "3.1"
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"text": "Agruments Directionality then prepared the manual annotations, Table 2 describes statistics of the RelEx dataset in detailed. Figure 2 show the distribution of relation types in training/development set and the test set. Due to the effect of adding 'strange' data to the test set, the rate is partly inconsistent between training/development and test set.",
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"section": "Relation",
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"text": "There are 6 human annotators to participate in the annotation process. An annotation guideline with full definition and illustrative examples was provided. We used a week to train annotators about the markable and non-markable cases in documents. In the following week, annotators conducted trial annotations, then raised some issues that need clarification. An expert then preliminarily assessed the quality of the trial annotation process before started the full annotation process.",
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"section": "Annotators and Annotation Tool",
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"text": "We used WebAnno 1 as the Annotation tool. It is a general purpose web-based annotation tool for a wide range of linguistic annotations including various layers of morphological, syntactical, and semantic annotations.",
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"text": "The annotators were divided into two groups and used their account to conduct independent annotations, i.e., each document was annotated at least twice. The annotation process is described in Figure 3 . First, the supervisor separated the whole dataset into several small parts. Each part was given to two independent annotators for annotating. For finding out the agreement between annotators, the committee then calculated the Inter-Annotator Agreement (IAA). Follow (Dalianis, 2018) , IAA can be carried out by calculating the Precision, Recall, F-score, and Cohen's kappa, between two annotators. If the IAA is very low, for example, F 1 is under 0.6, it may be due to the complexity and difficulty of the annotation task or the low quality of the annotation. For the RelEx task, the committee selected the IAA based on F 1, and chose an acceptable threshold of 0.7. If the IAA between two annotators on a subset was smaller than 0.7, we went through the curation process with a third annotator to decide the final annotation.",
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"text": "The test set are formatted similarly with the training and development data, but without information for the relation label. The task is to predict, given a sentence and two tagged entities, which of the relation labels to apply. The participated teams must submit the result in the same format with the training and development data.",
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"section": "Data Format and Submission",
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"text": "The participating systems had the following task: Given a documents and tagged entities, predict the semantic relations between those entities and the directions of the relations. Each teams can submit up to 3 runs for the evaluation.",
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"text": "The participated results were evaluated using standard metrics of Precision (P ), Recall (R) and F 1. In which, Precision indicates the percentage of system positives that are true instances, Recall indicates the percentage of true instances that the system has retrieved. F 1 is the harmonic mean of Recall and Precision, calculated as follows:",
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{
"text": "EQUATION",
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"raw_str": "F 1 = 2 \u00d7 P \u00d7 R P + R",
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"text": "We released a detailed scorer which outputs:",
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"text": "\u2022 A confusion matrix,",
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"sec_num": "4.2"
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"text": "\u2022 Results for the individual relations with P , R and F 1,",
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"sec_num": "4.2"
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"text": "\u2022 The micro-averaged P , R and F 1,",
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"sec_num": "4.2"
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"text": "\u2022 The macro-averaged P , R and F 1.",
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"sec_num": "4.2"
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"text": "Our official scoring metric is macro-averaged F 1, taking the directionality into account (except P ERSON AL \u2212 SOCIAL relations).",
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"section": "Evaluation Metrics",
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"text": "A total of 4 teams participated in the RelEx task. Since each team was allowed to submit up to 3 runs (i.e., 3 different version of their proposal method), a total of 12 runs were submitted. Table 3 lists the participants and provides a rough overview of the system features. Vn-CoreNLP 2 and underthesea 3 are used for preprocessing. All proposed model are based on the deep neural network architectures with different approaches, go from a simple method (i.e., multilayer perceptron) to Bidirectional Long Short-Term Memory and more complex architectures (e.g., BERT with entity start). With the application of deep learning models, participated teams use several pre-trained embedding model. In addition to word2vec (Mikolov et al., 2013; Vu, 2016) , RelEx challenge acknowledgement several BERTbased word embedding for Vietnamese, including PhoBERT (Nguyen and Nguyen, 2020), Nl-pHUST/vibert4news 4 , FPTAI/vibert (The et al., 2020) and XLMRoBERTa (Conneau et al., 2020) .",
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"text": "As shown in Table 4 , the macro-averaged F 1 score of participated teams (only considering the best run) ranges from 57.99% to 66.16% with an average of 62.42%. For reference information, the micro-averaged F 1 score ranges from 61.84% to 72.06% with an average of 66.99%. The highest macro-averaged P and R is 80.38% and 66.75%, respectively. However, the team with the highest P has quite low R, and vice versa, the team with the highest R has the lowest P . The first and secondranked teams have the right balance between P and R.",
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"text": "We ranked the teams by the performance of their best macro-averaged F 1 score. Team of Thuat Nguyen and Hieu Man Duc Trong from Hanoi University of Science and Technology, Hanoi, Vietnam submitted the best system, with a performance of 66.16% of F 1, i.e., 2.74% better than the runner-up system. The second prize was awarded to Pham Quang Nhat Minh with 63.42% of F 1. The third prize was awarded to SunBear Team from AI Research Team, R&D Lab, Sun Inc, who proposed many improvements in their model. The detailed results of all teams are shown in Table 5 .",
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"text": "We also analyze the performance for specific relations on the best results of each team for each relation. P ART \u2212 W HOLE seems to be the easiest relation. Comparing the best runs of teams, the lowest result for this relation is 79.57%, and the highest result was over 84.35%, i.e., the difference is comparatively small (4.78%). ORGAN IZAT ION \u2212 AF F ILIAT ION is the relation that has the most difference between the best and worst system (16.73%). The most challenging relation is P ERSON AL \u2212 SOCIAL. It is proved that being a problematic relation for all teams. This note can be clarified from the data statistics, although P ERSON AL \u2212 SOCIAL is a relation that has many different patterns in realistic, it accounts for only \u223c 5% of training and development data. It becomes even more difficult when it takes up \u223c 25% of test data. LOCAT ED follows P ERSON AL \u2212 SOCIAL in terms of difficulty. Some of its patterns are confused with the ORGAN IZAT ION \u2212 AF F ILIAT ION relation, i.e., whether a person is/do something in a particular location or is citizen/resident of a (geopolitical) location. An interesting observation shows that directional relations were not a difficult problem for participated teams. The submission with the most misdirected error failed only 7 examples out of the total number of results returned. Many submission does not have any errors in the directionality. Figure 4 shows the ratio between easy cases (correctly predicted in all runs), difficult cases (did not found by any run), the rest are the number of examples that correctly predicted in at least one run (but not all runs). There were 140 examples (\u223c 10%) that are classified incorrectly by all systems. Except for a handful of errors Num of examples predicted in 1-11 runs Figure 5 gives statistics on how many instances are correctly found in 1 to 11 out of 12 submissions. It shows that the proposed systems of participated teams produce multiple inconsistent results. It also notes the difficulty of the challenge and data.",
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"text": "The RelEx task was designed to compare different semantic relation classification approaches and provide a standard testbed for future research.",
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"text": "The RelEx dataset constructed in this task is expected to make significant contributions to the other related researches. RelEx challenge is an endorsement of machine learning methods based on deep neural networks. The participated teams have achieved some exciting and potential results. However, the deeper analysis also shows some performance limitations, especially in the case of semantic relations presented in a complex linguistic structure. This observation raises some research problems for future works. Finally, we conclude that the RelEx shared task was run successfully and is expected to contribute significantly to Vietnamese text mining and natural language processing communities.",
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"text": "https://github.com/vncorenlp 3 https://github.com/undertheseanlp 4 http://huggingface.co/NlpHUST/ vibert4news-base-cased",
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"text": "This work was supported by the Vingroup Innovation Foundation (VINIF) under the project code DA137_15062019/year 2019. The shared task committee would like to grateful DAGORAS data technology JSC. for their technical and financial support, and the six annotators for their hardworking to support the shared task.",
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},
{
"first": "Julie",
"middle": [],
"last": "Medero",
"suffix": ""
},
{
"first": "Kazuaki",
"middle": [],
"last": "Maeda",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "57",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. Ace 2005 multilin- gual training corpus. Linguistic Data Consortium, Philadelphia, 57:45.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "Relation examples."
},
"FIGREF1": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "The distribution of relation types in Datasets."
},
"FIGREF2": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "The annotation process."
},
"FIGREF4": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "The annotation process."
},
"FIGREF6": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "Number of examples predicted in 1-11 runs."
},
"TABREF1": {
"html": null,
"text": "Relation types permitted arguments and directionality.",
"content": "<table><tr><td/><td>Training set</td><td>Development set</td><td>Test set</td></tr><tr><td>Number of documents</td><td>506</td><td>250</td><td>300</td></tr><tr><td>LOCATED</td><td>612</td><td>346</td><td>294</td></tr><tr><td>PART-WHOLE</td><td>1176</td><td>514</td><td>815</td></tr><tr><td>PERSONAL -SOCIAL</td><td>102</td><td>98</td><td>449</td></tr><tr><td>ORGANIZATION -AFFILIATION</td><td>771</td><td>518</td><td>205</td></tr></table>",
"type_str": "table",
"num": null
},
"TABREF2": {
"html": null,
"text": "Statistics of the RelEx dataset.",
"content": "<table><tr><td>Data statistics</td><td/></tr><tr><td>Training + Development sets</td><td>Test set</td></tr><tr><td>LOCATED</td><td>PART-WHOLE</td></tr><tr><td colspan=\"2\">PERSONAL -SOCIAL AFFILIATION</td></tr></table>",
"type_str": "table",
"num": null
},
"TABREF4": {
"html": null,
"text": "Overview of the methods used by participating teams in RelEx task.",
"content": "<table><tr><td>Team</td><td>Macro-averaged P R F1</td><td>Micro-averaged P R F1</td></tr><tr><td colspan=\"3\">HT-HUS 73.54 62.34 66.16 76.17 68.37 72.06</td></tr><tr><td colspan=\"3\">MinhPQN 73.32 57.09 63.42 76.83 60.28 67.56</td></tr><tr><td>SunBear</td><td colspan=\"2\">58.44 66.75 62.09 60.82 73.29 66.48</td></tr><tr><td>VC-Tus</td><td colspan=\"2\">80.38 46.43 57.99 83.51 49.09 61.84</td></tr><tr><td/><td colspan=\"2\">Results are reported in %.</td></tr><tr><td colspan=\"3\">Highest result in each column is highlighted in bold.</td></tr></table>",
"type_str": "table",
"num": null
},
"TABREF5": {
"html": null,
"text": "The final results of participated teams (best run results).",
"content": "<table/>",
"type_str": "table",
"num": null
},
"TABREF6": {
"html": null,
"text": "HUS_1 62.74 72.33 84.05 40.43 78.76 57.90 64.89 80.49 63.82 71.19 HT-HUS_2 60.70 68.08 84.35 44.37 78.17 57.07 64.37 78.68 61.91 69.30 HT-HUS_3 62.50 74.60 82.87 44.67 73.54 62.34 66.16 76.17 68.37 72.06 MinhPQN_1 61.04 65.87 80.77 43.37 72.21 56.78 62.76 75.63 60.08 66.96 MinhPQN_2 62.41 66.38 81.00 43.87 73.32 57.09 63.42 76.83 60.28 67.56 MinhPQN_3 60.40 64.68 80.14 46.56 74.36 55.94 62.94 76.87 58.52 66.45 SunBear_1 59.74 67.54 79.57 41.50 58.44 66.75 62.09 60.82 73.29 66.48 SunBear_2 54.43 68.10 76.33 38.83 55.39 64.15 59.42 59.69 70.08 64.47 SunBear_3 49.29 62.10 71.52 31.24 53.11 55.27 53.54 55.91 59.16 57.49",
"content": "<table><tr><td>Team/Run</td><td>LOC AFF P-W F1 F1 F1</td><td>P-S F1</td><td>Macro-averaged P R F1</td><td>Micro-averaged P R F1</td></tr><tr><td>HT-VC-TUS_1</td><td colspan=\"4\">46.37 56.21 74.11 28.68 75.92 40.18 51.34 80.29 44.38 57.16</td></tr><tr><td>VC-TUS_2</td><td colspan=\"4\">55.23 57.87 79.70 39.16 80.38 46.43 57.99 83.51 49.09 61.84</td></tr><tr><td>VC-TUS_3</td><td colspan=\"4\">54.67 56.96 79.12 38.87 80.83 45.76 57.40 83.38 48.38 61.23</td></tr><tr><td/><td colspan=\"4\">Results are reported in %. Highest result in each column is highlighted in bold.</td></tr><tr><td/><td colspan=\"4\">LOC: LOCATED, AFF: ORGANIZATION-AFFILIATION,</td></tr><tr><td/><td/><td colspan=\"3\">P-W: PART-WHOLE, P-S: PERSONAL-SOCIAL.</td></tr></table>",
"type_str": "table",
"num": null
},
"TABREF7": {
"html": null,
"text": "Detailed results of all submissions. Sunbury] of LOCAT ED relation is misclassified either as ORGAN IZAT ION \u2212 AF F ILIAT ION or as no relation.",
"content": "<table><tr><td>caused by annotation errors, most of them are</td></tr><tr><td>made up of examples illustrating the limits of cur-</td></tr><tr><td>rent approaches. We need a more in-depth sur-</td></tr><tr><td>vey on linguistic patterns and knowledge, as well</td></tr><tr><td>as more complex reasoning techniques to resolve</td></tr><tr><td>these cases. A case in point: \"\u0110\u1eebng qu\u00ean trong tay</td></tr><tr><td>c\u1ee7a HLV Tom Thibodeau v\u1eabn c\u00f2n \u0111\u00f3 b\u1ed9 3 ng\u00f4i sao</td></tr><tr><td>Karl-Anthony Towns -Andrew Wiggins -Jimmy</td></tr><tr><td>Buter.\" (ID 24527838). In this instance, [Tom Thi-</td></tr><tr><td>bodeau] is participated in three P ERSON AL \u2212</td></tr><tr><td>SOCIAL relations with [Karl-Anthony Towns],</td></tr><tr><td>[Andrew Wiggins], and [Jimmy Buter]. In which,</td></tr><tr><td>two relations of [Tom Thibodeau] -[Andrew Wig-</td></tr><tr><td>gins] and [Tom Thibodeau] -[Jimmy Buter] were</td></tr><tr><td>not predicted by any team, probably on account</td></tr><tr><td>of their complex semantics presenting with a con-</td></tr><tr><td>junction. Another example: [Hassan \u0111\u01b0\u1ee3c cho l\u00e0</td></tr><tr><td>ng\u01b0\u1eddi Iraq , \u0111\u01b0\u1ee3c m\u1ed9t c\u1eb7p v\u1ee3 ch\u1ed3ng ng\u01b0\u1eddi Anh</td></tr><tr><td>nh\u1eadn l\u00e0m con nu\u00f4i v\u00e0 c\u00f9ng sinh s\u1ed1ng t\u1ea1i Sunbury</td></tr><tr><td>, v\u00f9ng ngo\u1ea1i \u00f4 London] (ID 23352918). Instance</td></tr><tr><td>[Hassan] -[</td></tr></table>",
"type_str": "table",
"num": null
}
}
}
} |