CAIR - NLP at S em E val-2023 Task 2: A Multi-Objective Joint Learning System for Named Entity Recognition (2023)

Abstract: This paper describes the NER system designed by the CAIR-NLP team for submission to Multilingual Complex Named Entity Recognition (MultiCoNER II) shared task, which presents a novel challenge of recognizing complex, ambiguous, and fine-grained entities in low-context, multi-lingual, multi-domain dataset and evaluation on the noisy subset. We propose a Multi-Objective Joint Learning System (MOJLS) for NER, which aims to enhance the representation of entities and improve label predictions through the joint implementation of a set of learning objectives. Our official submission MOJLS implements four objectives. These include the representation of the named entities should be close to its entity type definition, low-context inputs should have representation close to their augmented context, and also minimization of two label prediction errors, one based on CRF and another biaffine-based predictions, where both are producing similar output label distributions. The official results ranked our system 2nd in five tracks (Multilingual, Spanish, Swedish, Ukrainian, and Farsi) and 3 rd in three (French, Italian, and Portuguese) out of 13 tracks. Also evaluation of the noisy subset, our model achieved relatively better ranks. Official results indicate the effectiveness of the proposed MOJLS in dealing with the contemporary challenges of NER.

## introduction named entity recognition (ner) is an established natural language processing task that aims to locate entity mentions by marking entity-span boundaries and further classify them into corresponding entity types from a set of pre-defined entity categories in unstructured text @xcite . ner is a critical building block in information extraction, and knowledge-base construction pipeline @xcite . the se-meval 2023 task 2: multilingual complex named entity recognition (multiconer-ii) presented a novel challenge of building a ner system covering 33 fine-grained categories over 12 languages, namely english, french, spanish, german, italian, portuguese, ukrainian, swedish, chinese, farsi, hindi, bangla and also in multilingual setting in 13 tracks @xcite . multiconer-ii task presents a host of real-world contemporary challenges in terms of (1) low-context sentences (short and uncased text), (2) syntactically complex and ambiguous fine-grained entities, (3) entities having a large long-tail distribution and evolving nature, (4) multi-linguality, (5) evaluation on a noisy subset, where inputs are corrupted with noise either on context tokens or entity tokens, and (6) limited training examples per language. in this paper, we propose a multi-objective joint learning system (mojls) for ner, which aims to learn an enhanced representation of low-context, fine-grained entities and thereby improves recognition. we considered four training objectives. minimization of (1) representation gaps between the entities to the corresponding entity type definition (etd) using kl divergence loss, where etd is extracted from external knowledge bases, (2) representation gaps between input sentence and input augmented with entity context, extracted through external information (3) conditional random field (crf) label prediction loss using negative log-likelihood loss function (4) biaffine layer label prediction loss using cross-entropy (ce) loss function. our multilingual ner model is trained by fine-tuning a large multilingual pre-trained language model (plm) over the multilingual ner dataset by minimizing the combined losses from all the four loss functions. further, monolingual ner models are trained by fine-tuning the multilingual model using the corresponding monolingual data. we make the following observations based on experiments carried out. the official results show that the cair-nlp system ranked 2 nd in five tracks (multilingual, spanish, swedish, ukrainian, and farsi) and 3 rd in three tracks (french, italian, and portuguese) out of 13 tracks. also, our model achieved better ranks on noisy subset evaluation, 2nd in five tracks (spanish, swedish, french, italian, and portuguese) and 3rd in one track (english) out of the 8 tracks. related work is presented in the appendix section. ## system description this section introduces our multi-objective joint learning system for ner. the overall architecture of the proposed system is shown in figure 1 . the details of each building block are described below. ## conclusion for low-context, complex, ambiguous, and often noisy entities, relying solely on pre-trained language models to achieve competitive ner performance is insufficient. enhancement of entity context and refined representation of fine-grained entities using external knowledge bases and information sources are critical for achieving enhanced performance. a multi-objective joint learning strategy with suitable objectives for the overall label predictions along with set of intermediate objectives can enhance the performance. specifically for multiconer ii shared task, context augmentation improved the performance by 7.47%. the objective of having a representation of all fine-grained entities close to each other within an entity type, additionally enhanced performance by 2.78% and aided well in dealing with input noise and better generalization. adding other suitable objectives in our mojls formulation, better augmentation strategies, and use of external knowledge bases are likely to enhance performance further. tianyi zhang*, varsha kishore*, felix wu*, kilian q. weinberger, and yoav artzi. 2020. bertscore: evaluating text generation with bert. in international conference on learning representations. appendix 6.1 dataset multiconer ii dataset comprises of 6 coarsegrained and 33 fine-grained categories across 12 languages.