Cross-lingual Semantic Role Labelling with the V alpal database knowledge (2022)

Abstract: Cross-lingual Transfer Learning typically involves training a model on a high-resource source language and applying it to a low-resource target language. In this work we introduce a lexical database called Valency Patterns Leipzig (ValPal) which provides the argument pattern information about various verb-forms in multiple languages including low-resource languages. We also provide a framework to integrate the ValPal database knowledge into the state-of-the-art LSTM based model for cross-lingual semantic role labelling. Experimental results show that integrating such knowledge resulted in am improvement in performance of the model on all the target languages on which it is evaluated.

## introduction semantic role labeling (srl) is the task of identifying various semantic arguments such as agent, patient, instrument, etc. for each of the target verb (predicate) within an input sentence. srl is useful as an intermediate step in numerous high level nlp tasks, such as information extraction @xcite , automatic document categorization @xcite ), text-summarization @xcite question-answering @xcite etc. state of the art approaches to srl such as @xcite @xcite are supervised approaches which require a large annotated dataset to be trained on, thus limiting their utility to only high-resorce languages. this issue of data-sparsity (in low-resource languages) has been effectively addressed with numerous cross-lingual approaches to srl including annotation projection approaches @xcite @xcite , model transfer approaches @xcite @xcite and machine translation approaches @xcite . in this work, we use the valency patterns leipzig (valpal) online database 1 @xcite which is a multilingual lexical database, originally created by the linguistic research community to study the similarities and differences in verb-patterns for various world languages. furthermore, we provide a framework to utilise the knowledge available in valpal database to improve the performance of the state-of-the-art cross-lingual approach to srl task. ## valpal database valency patterns leipzig (valpal) is a comprehensive multilingual lexical database which provides semantic and syntactic information about different verb-forms in various languages including many low-resource languages. the valpal database provides values for the following features for each verb-form: ## fol rules from valpal to inject the entire valpal database knowledge about any low-resource target-language l in a cross-lingual neural network model, we represented this knowledge as a set of first-order-logic (fol) rules f l . the process of generating this set of fol rules involves two steps namely translating valpal argument-patterns to propbank label orders and writing propbank-label order as fol rule described in sections 3.1 and 3.2. in valpal database, the argument-pattern for verb-verbform lang argumentpattern alterations (alteration-name:arg-pattern (example) ) cook english 1nom > v.subj[1] > 2acc understood omitted object:1nom > v.subj[1] > 2acc (she walked in while i was cooking.) causative-inchoative : 2acc > v.subj[1] (the soup is still cooking.) kochen german 1nom > v.subj[1] > 2acc benefactive alternation:1nom > v ′ > subj[1] > 3-@xmath0.) be-alternation:1-@xmath1.subj[1] > 4acc > mit + 2dat (die großmutter bekocht die kranke mit suppe.) ## experiments this section described the experiments performed by us to evaluate the proposed model. ## conclusion valency patterns leipzig (valpal) is a multilingual lexical database which provides the knowledge about the argument-patterns of various verbforms in multiple languages including numerous low-resource languages. the database is originally created by the linguistic community to study the similarities and differences in the verb-patterns for various world's languages. in this work we utilised this database to improve the performance of the state-of-the-art cross-lingual model for srl task. we evaluated a framework to integrate the entire valpal knowledge about any low-resource targetlanguage into an lstm based model. our proposed framework only requires an unannotated target language corpus for the knowledge integration.