A Multi-task Model for Multilingual Trigger Detection and Classification (2019)

Abstract: In this paper we present a deep multi-task learning framework for multilingual event and argument trigger detection and classification. In our current work, we identify detection and classification of both event and argument triggers as related tasks and follow a multi-tasking approach to solve them simultaneously in contrast to the previous works where these tasks were solved separately or learning some of the above mentioned tasks jointly. We evaluate the proposed approach with multiple low-resource Indian languages. As there were no datasets available for the Indian languages, we have annotated disaster related news data crawled from the online news portal for different low-resource Indian languages for our experiments. Our empirical evaluation shows that multi-task model performs better than the single task model, and classification helps in trigger detection and vice-versa.

## introduction event extraction is an important task in natural language processing (nlp). an event can be an occurrence happening in certain place during a particular interval of time. in text, the word or phrase that describes an event is called event trigger. argument of an event refers to the attributes such as the location, time of occurrence of the event, participants involved and so on. therefore event trigger detection, event trigger classification, argument trigger detection and argument trigger classification are the four important sub-tasks of event extraction. in our current paper, we have solved all the four problems using a multi-task architecture. multi-task learning (mtl), which essentially means performing more than one related task simultaneously, has been proven to be effective for various nlp tasks in recent times @xcite . the key idea behind mtl is that the inductive transfer of knowledge, learned for a particular task, can help to improve the performance of another task by means of parameter sharing between tasks. according to @xcite , "mtl improves generalization by leveraging the domainspecific information contained in the training signals of related tasks". in our current work, we have identified detection and classification of both event and arguments as two related tasks. as both event and argument trigger detection are sequence labelling problems, we have merged those two sub-tasks into one and used a single loss function. for the same reason, we have merged event and argument trigger classification task into one task and used another loss function. thus in our proposed architecture, even though we have two main tasks for learning shared representation, we have basically solved four sub-tasks viz. event trigger detection, event trigger classification, argument detection and argument classification. our proposed architecture has two variants which are further discussed later in this paper. as we are working with low-resource languages which have data sparsity issue, we have proposed a multi-task, multi-lingual architecture which is trained on both hindi and bengali data. due to unavailability of training data in these two languages, we have annotated disaster related news data crawled from online news portals for our experiments. ## related works being a very important problem in nlp, event extraction has already been explored by the research community for a long time. some feature based approaches have decomposed the entire event extraction task into two sub-tasks and solved them separately @xcite @xcite . but the main problem of this approach is error propagation which is dealt by @xcite , @xcite , @xcite , @xcite using a joint event extraction algorithm. however both of the above approaches have used hand-designed feature. @xcite propose a convolutional neural network (cnn) for automatic feature extraction. @xcite introduce a dynamic multi-pooling cnn which uses a dynamic multi-pooling layer according to event triggers and arguments in multi-event sentences, to capture more crucial information. in another work, @xcite propose a skip-gram based cnn model which allows nonconsecutive convolution. @xcite propose a forward-backward recurrent neural network (rnn) to detect event triggers which can be in the form of both words or phrases. @xcite propose a language independent neural network which uses both cnn and bi-lstm for event detection. @xcite propose to improve the performance of event detection by using the events automatically detected from framenet. though these neural based systems perform well, they still suffer from error propagation issue. to overcome this issue, @xcite propose a joint framework with bidirectional rnn. however @xcite observe that joint model achieves insignificant improvements on event detection task. they analyze the problem of joint models on the task of event detection, and propose to use the annotated argument information explicitly for this task. @xcite also propose a joint model for event and entity extraction but in document level instead of sentence level in contrast to most of the previous works. in recent years @xcite introduce a cross language attention model for event detection where they focus on english and chinese. @xcite propose a novel framework to jointly extract multiple event triggers and arguments. @xcite propose a novel dependency bridge rnn which includes syntactic dependency relationships. dependency relationship is also used by @xcite . they investigate a cnn based on dependency trees to perform event detection. @xcite present a gated recurrent unit (gru) based model that combines both temporal structure along with syntactic information through an attention mechanism. event extraction task has also been addressed in specialized tracks dedicated in text analysis conference (tac). event extraction in disaster domain in english language is reported in @xcite @xcite @xcite . however, significant attempt to build event extraction system in indian languages is lacking. in recent times, some of the works are reported in @xcite @xcite . to the best of our knowledge, this is the first attempt to solve four important subtasks of event extraction viz. event trigger detection, event trigger classification, argument trigger detection and argument trigger classification simultaneously in a multi-task, multi-lingual setting. ## task description and contributions in this paper, we propose a multi-task, multilingual trigger detection and classification method for hindi and bengali in disaster related news data. for a given hindi/bengali sentence, we perform the following tasks simultaneously: (a) event trigger detection: word or phrase that describes an event is called event trigger. detecting event triggers is a sequence labeling task. but we formulate our current approach as a multi-class classification task as in @xcite . argument detection is also a sequence labeling task. like event detection, we also formulate this task as a multi-class classification problem. in most of the previous works, both event and argument detection are considered as two separate tasks. however in our current work, we combine both the tasks into a single task based on our observation. detailed analysis of news articles reveal the fact that each type of event triggers along with its corresponding arguments follow a particular pattern in a sentence. in the first example, the sentence contains place argument द ली (delhi) and time argument शाम 6 बजे (6pm). each type of argument is followed by a type specific post-position ('म ' for place argument and 'के ' for time argument). in second example the sentence contains event specific argument like magnitude (7.2) of earthquake along with place argument इं डो नशया (indonesia). this type of patterns are often seen in news documents. so it is intuitive to consider both event and argument trigger detection as a single task. for classification also, we merge both the event trigger classification and argument trigger classification as a single task. in this way, we learn all the four above mentioned tasks simultaneously using two loss functions. we perform our experiments using both hindi and bengali news datasets in mono-lingual as well as multi-lingual settings. we compare our multi-task learning (mtl) results with single-task learning (stl) results for the above mentioned mono-lingual and multi-lingual settings. for most of the cases we are getting 2% to 7% performance improvement in detection task. however for classification task, we see that the performance improves for some of the classes and for the remaining classes, the model does not perform at par with the other classes. two contributions of our paper are • a multi-task, multi-lingual approach for event extraction in hindi and bengali for disaster domain. our proposed system has two variants -(a) the classification output helping in detection (mt1). (b) the detection output helping in classification (mt2). both the architectures are discussed in methodology section. the following examples show that each type of event and argument trigger is followed by semantically similar kind of words in a sentence. we highlight the event trigger and different types of argument triggers using different colour codes for better readability. ## methodology our proposed models take sentence of the form [w 0 ,w 1 ,...,w n ] as input. it produces two outputs for two main tasks namely detection (both event and argument) and classification (both event and argument). the detection task predicts the event or argument label (l i ) for each word (w i ) where l @xmath0,o,b foot_0 . as we formulate detection as a multiclass classification task even though it being a sequence labeling task, we use softmax classifier at the final layer. for classification task also, we use softmax classifier at the final layer to classify event and argument trigger into their predefined types. we employ a hard parameter sharing strategy @xcite . we use a shared bidirectional long short-time memory (bi-lstm) @xcite to capture the contextual information of each word. figure 1a illustrates the design of first variant of our proposed architecture. here the classification output of each word is concatenated with the corresponding representation resulting from the shared bi-lstm and fed as input to the final detection layer of that word. this is done with the intuition of improving the detection results with the help of classification output. for example if a word is classified as 'none' then it has higher chance of being outside event or argument trigger boundaries. in subsequent sections, we call this architecture as mt1. figure 1b illustrates the design of second variant of our proposed architecture. here the detection output of each word is concatenated with the corresponding representation of the shared bi-lstm and fed as input to the final classification layer. this is done with the intuition of improving the classification results with the help of detection output. ## results and analysis ## conclusion and future works in this paper, we present a multi-tasking, multilingual architecture for simultaneous detection and classification of event and argument triggers. we have proposed two variants where in each one of them, one task is helping another related task. our results show that related tasks can definitely share information between them. we also compare our approach with separate models which can be employed for event and argument trigger detection respectively. other future works include developing an endto-end system which will consist of a multi-tasking system such that given a sentence as input, event and argument triggers will be extracted from it and if there exists any link between the extracted event and argument, then the output of the system will be positive and otherwise negative.