pwc-archive/papers-with-abstracts huggingface dataset:
NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
Authors: Amina Miftahova, Alexander Pugachev, Artem Skiba, Katya Artemova, Tatiana Batura, Pavel Braslavski, Vladimir Ivanov
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Decoder, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), TAG, token-classification, Token Classification

This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual language models differently. The first approach follows the token classification schema, in which each token is assigned with a tag. The second approach follows a recent template-free paradigm, in which an encoder-decoder model translates the input sequence of words to a special output, encoding named entities with predefined labels. We utilize RemBERT and mT5 as backbone models for these two approaches, respectively. Our results show that the oldie but goodie token classification outperforms the template-free method by a wide margin. Our code is available at: https://github.com/Abiks/MultiCoNER.
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition
Authors: Atharvan Dogra, Prabsimran Kaur, Guneet Kohli, Jatin Bedi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering, Translation, Word Embeddings

Named Entity Recognition (NER), an essential subtask in NLP that identifies text belonging to predefined semantics such as a person, location, organization, drug, time, clinical procedure, biological protein, etc. NER plays a vital role in various fields such as informationextraction, question answering, and machine translation. This paper describes our participating system run to the Named entity recognitionand classification shared task SemEval-2022. The task is motivated towards detecting semantically ambiguous and complex entities in shortand low-context settings. Our team focused on improving entity recognition by improving the word embeddings. We concatenated the word representations from State-of-the-art language models and passed them to find the best representation through a reinforcement trainer. Our results highlight the improvements achieved by various embedding concatenations.
SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task
Authors: Fadi Hassan, Wondimagegnhue Tufa, Guillem Collell, Piek Vossen, Lisa Beinborn, Adrian Flanagan, Kuan Eeik Tan
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents our system used to participate in task 11 (MultiCONER) of the SemEval 2022 competition. Our system ranked fourth place in track 12 (Multilingual) and fifth place in track 13 (Code-Mixed). The goal of track 12 is to detect complex named entities in a multilingual setting, while track 13 is dedicated to detecting complex named entities in a code-mixed setting. Both systems were developed using transformer-based language models. We used an ensemble of XLM-RoBERTa-large and Microsoft/infoxlm-large with a Conditional Random Field (CRF) layer. In addition, we describe the algorithms employed to train our models and our hyper-parameter selection. We furthermore study the impact of different methods to aggregate the outputs of the individual models that compose our ensemble. Finally, we present an extensive analysis of the results and errors.
NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model
Authors: Lung-Hao Lee, Chien-Huan Lu, Tzu-Mi Lin
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Position

This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams.
CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification
Authors: Keyu Pu, Hongyi Liu, Yixiao Yang, Jiangzhou Ji, Wenyi Lv, Yaohan He
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Boundary Detection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition
Authors: Hyunju Song, Steven Bethard
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Data Augmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper presents the approaches and systems of the UA-KO team for the Korean portion of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition.We fine-tuned Korean and multilingual BERT and RoBERTA models, conducted experiments on data augmentation, ensembles, and task-adaptive pretraining. Our final system ranked 8th out of 17 teams with an F1 score of 0.6749 F1.
L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition
Authors: Emanuela Boros, Carlos-Emiliano González-Gallardo, Jose Moreno, Antoine Doucet
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Language Modeling, Language Modelling, Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the high-resource languages and lower than average for low-resource and multilingual models.
MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder
Authors: Ehsan Tavan, Maryam Najafi
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The multilingual complex named entity recognition task of SemEval2020 required participants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text language model and its multilingual version, MT5, along with the transformer’s encoder module. The subtoken check has also been introduced, resulting in a 4% increase in the model F1-score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each scenario within the English development set. Our model is also evaluated in other languages.
PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions
Authors: Long Ma, Xiaorong Jian, Xuan Li
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Binary Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes our system used in the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard. We propose Dictionary-fused BERT, a flexible approach for entity dictionaries integration. The main ideas of our systems are:1) integrating external knowledge (an entity dictionary) into pre-trained models to obtain contextualized word and entity representations 2) designing a robust loss function leveraging a logit matrix 3) adding an auxiliary task, which is an on-top binary classification to decide whether the token is a mention word or not, makes the main task easier to learn. It is worth noting that our system achieves an F1 of 0.914 in the post-evaluation stage by updating the entity dictionary to the one of (CITATION), which is higher than the score of 1st on the leaderboard of the evaluation stage.
SemEval 2022 Task 12: Symlink - Linking Mathematical Symbols to their Descriptions
Authors: Viet Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Nguyen
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Knowledge Base Construction

We describe Symlink, a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents. This is a new task in SemEval 2022, which attracted 180 individual registrations and 59 final submissions from 7 participant teams. We expect the data developed for this task and the findings reported to be valuable for the scientific knowledge extraction and automated knowledge base construction communities. The data used in this task is publicly accessible at https://github.com/nlp-oregon/symlink.
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions
Authors: Sung-Min Lee, Seung-Hoon Na
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Joint Entity and Relation Extraction, Machine Reading Comprehension, NER, Reading Comprehension, Relation Extraction

This paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) and relation extraction (RE). Our system is a two-stage pipeline model based on SciBERT that detects symbols, descriptions, and their relationships in scientific documents. The system consists of 1) machine reading comprehension(MRC)-based NER model, where each entity type is represented as a question and its entity mention span is extracted as an answer using an MRC model, and 2) span pair classification for RE, where two entity mentions and their type markers are encoded into span representations that are then fed to a Softmax classifier. In addition, we deploy a rule-based symbol tokenizer to improve the detection of the exact boundary of symbol entities. Regularization and ensemble methods are further explored to improve the RE model.
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities
Authors: Nicholas Popovic, Walter Laurito, Michael Färber
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Joint Entity and Relation Extraction, Math, Relation, Relation Extraction, valid

In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in sequence, our system incorporates information from relation extraction into entity extraction. This means that the system can be trained even on data sets where only a subset of all valid entity spans is annotated. We provide an extensive evaluation of the proposed system and its strengths and weaknesses. Our approach, which can be scaled dynamically in computational complexity at inference time, produces predictions with high precision and reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the domain of physics and math, it achieves high relation extraction macro F1 scores of 95.43% and 79.17%, respectively. The code used for training and evaluating our models is available at: https://github.com/nicpopovic/RE1st
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets
Authors: Rob Goot
Date: 2022-07-01 00:00:00
Proceeding: SemEval (NAACL) 2022 7
Tasks: Multi-Task Learning, Sentence

Previous work on multi-task learning in Natural Language Processing (NLP) oftenincorporated carefully selected tasks as well as carefully tuning ofarchitectures to share information across tasks. Recently, it has shown thatfor autoregressive language models, a multi-task second pre-training step on awide variety of NLP tasks leads to a set of parameters that more easily adaptfor other NLP tasks. In this paper, we examine whether a similar setup can beused in autoencoder language models using a restricted set of semanticallyoriented NLP tasks, namely all SemEval 2022 tasks that are annotated at theword, sentence or paragraph level. We first evaluate a multi-task model trainedon all SemEval 2022 tasks that contain annotation on the word, sentence orparagraph level (7 tasks, 11 sub-tasks), and then evaluate whetherre-finetuning the resulting model for each task specificially leads to furtherimprovements. Our results show that our mono-task baseline, our multi-taskmodel and our re-finetuned multi-task model each outperform the other modelsfor a subset of the tasks. Overall, huge gains can be observed by doingmulti-task learning: for three tasks we observe an error reduction of more than40%.
Introducing QuBERT: A Large Monolingual Corpus and BERT Model for Southern Quechua
Authors: Rodolfo Zevallos, John Ortega, William Chen, Richard Castro, Núria Bel, Cesar Toshio, Renzo Venturas, Hilario Aradiel and Nelsi Melgarejo
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7

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Improving Distantly Supervised Document-Level Relation Extraction Through Natural Language Inference
Authors: Clara Vania, Grace Lee and Andrea Pierleoni
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Document-level Relation Extraction, Natural Language Inference, Relation, Relation Extraction

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Generating unlabelled data for a tri-training approach in a low resourced NER task
Authors: Hugo Boulanger, Thomas Lavergne and Sophie Rosset
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: NER

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ANTS: A Framework for Retrieval of Text Segments in Unstructured Documents
Authors: Brian Chivers, Mason P. Jiang, Wonhee Lee, Amy Ng, Natalya I. Rapstine and Alex Storer
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Retrieval

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Help from the Neighbors: Estonian Dialect Normalization Using a Finnish Dialect Generator
Authors: Mika Hämäläinen, Khalid Alnajjar and Tuuli Tuisk
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7

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Building an Event Extractor with Only a Few Examples
Authors: Pengfei Yu, Zixuan Zhang, Clare Voss, Jonathan May and Heng Ji
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7

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AfriTeVA: Extending ?Small Data? Pretraining Approaches to Sequence-to-Sequence Models
Authors: Odunayo Jude Ogundepo, Akintunde Oladipo, Mofetoluwa Adeyemi, Kelechi Ogueji and Jimmy Lin
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7

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Few-shot Learning for Sumerian Named Entity Recognition
Authors: Guanghai Wang, Yudong Liu and James Hearne
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Few-Shot Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

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Deep Learning-Based Morphological Segmentation for Indigenous Languages: A Study Case on Innu-Aimun
Authors: Ngoc Tan Le, Antoine Cadotte, Mathieu Boivin, Fatiha Sadat and Jimena Terraza
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7

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Unsupervised Knowledge Graph Generation Using Semantic Similarity Matching
Authors: Lixian Liu, Amin Omidvar, Zongyang Ma, Ameeta Agrawal and Aijun An
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Graph Generation, Semantic Similarity, Semantic Textual Similarity

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Alternative non-BERT model choices for the textual classification in low-resource languages and environments
Authors: Syed Mustavi Maheen, Moshiur Rahman Faisal, Md. Rafakat Rahman and Md. Shahriar Karim
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Classification

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Generating Complement Data for Aspect Term Extraction with GPT-2
Authors: Amir Pouran Ben Veyseh, Franck Dernoncourt, Bonan Min and Thien Huu Nguyen
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Term Extraction

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How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train & Few-shot Cross-lingual Transfer
Authors: Iman Jundi and Gabriella Lapesa
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: Cross-Lingual Transfer

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Unified NMT models for the Indian subcontinent, transcending script-barriers
Authors: Gokul N.C.
Date: 2022-07-01 00:00:00
Proceeding: DeepLo 2022 7
Tasks: NMT

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An Encoder Attribution Analysis for Dense Passage Retriever in Open-Domain Question Answering
Authors: Minghan Li, Xueguang Ma, Jimmy Lin
Date: 2022-07-01 00:00:00
Proceeding: NAACL (TrustNLP) 2022 7
Tasks: Open-Domain Question Answering, Question Answering, Retrieval

The bi-encoder design of dense passage retriever (DPR) is a key factor to its success in open-domain question answering (QA), yet it is unclear how DPR’s question encoder and passage encoder individually contributes to overall performance, which we refer to as the encoder attribution problem. The problem is important as it helps us identify the factors that affect individual encoders to further improve overall performance. In this paper, we formulate our analysis under a probabilistic framework called encoder marginalization, where we quantify the contribution of a single encoder by marginalizing other variables. First, we find that the passage encoder contributes more than the question encoder to in-domain retrieval accuracy. Second, we demonstrate how to find the affecting factors for each encoder, where we train DPR with different amounts of data and use encoder marginalization to analyze the results. We find that positive passage overlap and corpus coverage of training data have big impacts on the passage encoder, while the question encoder is mainly affected by training sample complexity under this setting. Based on this framework, we can devise data-efficient training regimes: for example, we manage to train a passage encoder on SQuAD using 60% less training data without loss of accuracy.
Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
Authors: Kathleen C. Fraser, Svetlana Kiritchenko, Esma Balkir
Date: 2022-07-01 00:00:00
Proceeding: NAACL (TrustNLP) 2022 7
Tasks: Philosophy

In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong. This is typically done in a bottom-up fashion, by exposing the model to different scenarios, annotated with human moral judgements. One question, however, is whether the trained models actually learn any consistent, higher-level ethical principles from these datasets – and if so, what? Here, we probe the Allen AI Delphi model with a set of standardized morality questionnaires, and find that, despite some inconsistencies, Delphi tends to mirror the moral principles associated with the demographic groups involved in the annotation process. We question whether this is desirable and discuss how we might move forward with this knowledge.
The Cycle of Trust and Responsibility in Outsourced AI
Authors: Maximilian Castelli, Linda C. Moreau, Ph.D.
Date: 2022-07-01 00:00:00
Proceeding: NAACL (TrustNLP) 2022 7
Tasks: Ethics

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming must-have capabilities. According to a 2019 Forbes Insights Report, “seventy-nine percent [of executives] agree that AI is already having a transformational impact on workflows and tools for knowledge workers, but only 5% of executives consider their companies to be industry-leading in terms of taking advantage of AI-powered processes.” (Forbes 2019) A major reason for this may be a shortage of on-staff expertise in AI/ML. This paper explores the intertwined issues of trust, adoption, training, and ethics of outsourcing AI development to a third party. We describe our experiences as a provider of outsourced natural language processing (NLP). We discuss how trust and accountability co-evolve as solutions mature from proof-of-concept to production-ready.
Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers
Authors: Edoardo Mosca, Katharina Harmann, Tobias Eder, Georg Groh
Date: 2022-07-01 00:00:00
Proceeding: NAACL (TrustNLP) 2022 7

Large-scale surveys are a widely used instrument to collect data from a target audience. Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers. Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment. This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open- and closed-ended questions. We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model. Experiments on EMS—a survey-based study researching influencing factors affecting a student’s career goals—show that the proposed approach can identify such factors both at the input- and higher concept-level.