BILSTM

Graph Convolution for Multimodal Information Extraction from Visually Rich Documents (N19-2)

Challenge:	Existing models to classify rumors have low precision and are time consuming.
Approach:	They propose a multiloss hierarchical biLSTM model with an attenuation factor that can extract deep information from limited quantities of text.
Outcome:	The proposed model can extract deep information from limited quantities of text.

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Xiaojing Liu, Feiyu Gao, Qiong Zhang, Huasha Zhao

Multimodality

Challenge:	Visually rich documents (VRDs) present information in the form of both text and vision.
Approach:	They propose a graph convolution based model to combine textual and visual information presented in VRDs.
Outcome:	The proposed model outperforms existing models on two real-world datasets.

Opinion Mining with Deep Contextualized Embeddings (N19-3)

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Wen-Bin Han, Noriko Kando

Towards a Standardized Dataset on Indonesian Named Entity Recognition (2020.aacl-srw)

Challenge:	Existing methods for opinion expression detection are based on token-level sequence labeling .
Approach:	They propose to use BERT and conditional random field embedders to detect opinion expressions.
Outcome:	The proposed model outperforms ELMo embedders in opinion expression detection.

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Siti Oryza Khairunnisa, Aizhan Imankulova, Mamoru Komachi

The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (P19-1)

Challenge:	Named entity recognition (NER) tasks in the Indonesian language are still lacking data for the majority of languages, including Indonesian.
Approach:	They re-annotated an open dataset with 2,000 sentences and compared the results with a bidirectional long short-term memory and conditional random field approach.
Outcome:	The proposed approach improved the prediction score and consistent organization tag for the Indonesian language.

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Agnieszka Falenska, Jonas Kuhn

Does Named Entity Recognition Truly Not Scale Up to Real-world Product Attribute Extraction? (2023.emnlp-industry)

Challenge:	Existing non-neural dependency parsers benefit from information coming from structural features . however, their successors lack explicit information about the structural context .
Approach:	They propose to model the structural context of a biLSTM-based dependency parser with features built from partial subtrees.
Outcome:	The proposed models do not use any conventional structural features but capture implicitly the structural context.

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Wei-Te Chen, Keiji Shinzato, Naoki Yoshinaga, Yandi Xia

Question Answering

Challenge:	scalability of attribute-value extraction (AVE) task is key for a large number of products . a question-answering (QA)-based approach is better for AVE, but requires a larger number of classes to be scalable.
Approach:	They propose a question-answering-based approach that additionally inputs the target attribute as a query to extract its values.
Outcome:	The proposed approach outperforms a classical approach on real-word e-commerce datasets in accuracy and speed.

JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models (D19-50)

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Hani Al-Omari, Malak Abdullah, Ola AlTiti, Samira Shaikh

Responsible & Trustworthy NLP

Challenge:	Detecting fake news is not well established yet, but it can be classified under several labels: false, biased, or framed to mislead the readers.
Approach:	They propose a deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda using a corpus from a challenge.
Outcome:	The proposed model outperforms the baseline model on a dataset from the challenge NLP4IF 2019 .

A k-Nearest Neighbor Approach towards Multi-level Sequence Labeling (N19-2)

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Yue Chen, John Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for complex dialog management require limited training data.
Approach:	They propose a method for intent recognition for complex dialog management in low resource situations . they use windowed word n-grams, POS tag n grams and pre-trained word embeddings as features .
Outcome:	The proposed method performs better with less than 1% of the data size than existing methods but requires considerably more data.

Emotion Classification in a Resource Constrained Language Using Transformer-based Approach (2021.naacl-srw)

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Avishek Das, Omar Sharif, Mohammed Moshiul Hoque, Iqbal H. Sarker

A Named Entity Recognition Shootout for German (P18-2)

Challenge:	Existing methods to classify Bengali text into six basic emotions are infancy for resource-constrained languages like English, Arabic, Chinese and French.
Approach:	They propose a transformer-based technique to classify Bengali text into one of the six basic emotions: anger, fear, disgust, sadness, joy, and surprise.
Outcome:	The proposed technique outperforms all other techniques by achieving highest weighted f_1-score on the test data.

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Martin Riedl, Sebastian Padó

TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers (2022.acl-srw)

Challenge:	Named entity recognition and classification (NER) is a central component in many natural language processing pipelines.
Approach:	They propose to build a model for German named entity recognition that performs at the state of the art for both contemporary and historical texts.
Outcome:	The proposed model outperforms the CRF and BiLSTM on large and small datasets.

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Suma Reddy Duggenpudi, Subba Reddy Oota, Mounika Marreddy, Radhika Mamidi

Neural Metaphor Detection with Visibility Embeddings (2021.starsem-1)

Challenge:	Named Entity Recognition (NER) is a successful and well-researched problem in English due to the availability of resources.
Approach:	They propose to use two annotated NER datasets for the Telugu language . they compare the finetuned Telugus model with the existing model in NER .
Outcome:	The proposed models outperform existing models on a large dataset of 38,363 sentences on telugu and other languages.

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Gitit Kehat, James Pustejovsky

Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model (D19-50)

Challenge:	Using Visibility Embeddings, sequence metaphor labeling is improved . many metaphors involve noticeable differences between the abstractness of words constructing them .
Approach:	They propose to concatenate sequence metaphor labeling with BiLSTM inputs to obtain improvements . they use visibility embeddings to provide a good estimation of a word's concreteness .
Outcome:	The proposed method improves the problem of sequence metaphor labeling with BERT . it allows for consistent and significant improvements at almost no cost .

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George-Alexandru Vlad, Mircea-Adrian Tanase, Cristian Onose, Dumitru-Clementin Cercel

Reevaluating Argument Component Extraction in Low Resource Settings (D19-61)

Challenge:	a new task is needed to detect propaganda in news articles . the need for communication has increased in online social media platforms . a proposed solution to the problem of sentence-level propaganda classification is ranked 12th .
Approach:	They propose to build a binary classifier able to provide corresponding propaganda labels . their solution ranks 12th among 26 teams in the NLP4IF-2019 Shared Task SLC .
Outcome:	The proposed model outperforms baseline approach and the winning system on a similar task.

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Anirudh Joshi, Timothy Baldwin, Richard Sinnott, Cecile Paris

UZH@CRAFT-ST: a Sequence-labeling Approach to Concept Recognition (D19-57)

Challenge:	Argument component extraction is a challenging and complex high-level semantic extraction task.
Approach:	They propose to use character-level, GloVe, ELMo, and BERT encodings to compare arguments extracted using standard BiLSTM-CRF encoders.
Outcome:	The proposed approaches perform better than baselines in higher-level semantic extraction tasks and suggest future improvements.

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Lenz Furrer, Joseph Cornelius, Fabio Rinaldi

Bridging CNNs, RNNs, and Weighted Finite-State Machines (P18-1)

Challenge:	CRAFT shared task 2019: concept recognition using named entity recognition and normalization . a biLSTM-based network and a transformer system were used to tackle both tasks in a single model .
Approach:	They propose two different neural approaches to concept recognition . they propose a BiLSTM-based network and a bioBERT-based system for NER and normalization .
Outcome:	The proposed systems model the task as a sequence labeling problem.

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Roy Schwartz, Sam Thomson, Noah A. Smith

Challenge:	recurrent and convolutional neural networks are useful for encoding natural language utterances.
Approach:	They propose a model that combines neural representation learning with weighted finite-state automatas to learn a soft version of traditional surface patterns.
Outcome:	The proposed model is comparable or better than a BiLSTM baseline and a CNN baseline on three text classification tasks.

Sentiment Analysis of Yelp Review Dataset: A Comparative Study of Machine Learning Methods (2026.acl-srw)

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Krishna Thakar, Mohamed Abu Sheha, Emmanuel Thompson

Language Models for Code-switch Detection of te reo Māori and English in a Low-resource Setting (2022.findings-naacl)

Challenge:	Existing methods for sentiment analysis are inconsistent and require manual processing.
Approach:	They use natural language processing and machine learning to classify Yelp reviews' sentiments.
Outcome:	The proposed model outperforms other models on Yelp reviews.

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Jesin James, Vithya Yogarajan, Isabella Shields, Catherine Watson, Peter Keegan, Keoni Mahelona, Peter-Lucas Jones

Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding (N18-2)

Challenge:	Te reo Mori is New Zealand’s only indigenous language spoken by 4.5% of the population of 5 million.
Approach:	They train bilingual sub-word embeddings to detect Mori-English code-switching points using a cloud-based multilingual system such as Google and Microsoft Azure.
Outcome:	The proposed model outperforms large-scale contextual models on down streaming tasks of detecting Mori language.

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Huasha Zhao, Yi Yang, Qiong Zhang, Luo Si

Low-Resource NLP

Challenge:	Entity recognition is a widely benchmarked task in natural language processing . a neural architecture called BiLSTM-CRF is used to model the language sequences .
Approach:	They propose a neural architecture called BiLSTM-CRF to model the language sequences.
Outcome:	The proposed system achieves state-of-the-art on English entity recognition task and also in other languages.

Neural Metaphor Detection in Context (D18-1)

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Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer

TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos (2026.findings-acl)

Challenge:	Existing models focus on limited forms of linguistic context, such as unigrams.
Approach:	They propose end-to-end neural models for detecting metaphorical word use in context . they show that bi-directional biLSTM models which operate on complete sentences work well .
Outcome:	The proposed models show that they can learn rich contextual word representations . they are compared to previous models which focused on limited linguistic context .

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Fawad Javed Fateh, Umer Ahmed, Hamza Khan, Zeeshan Zia, Quoc-Huy Tran

Visual Data in NLP

Challenge:	Several video understanding applications require the ability of temporal reasoning.
Approach:	They propose a video large language model for temporal reasoning and fine-grained understanding in long videos.
Outcome:	The proposed model outperforms existing methods in time and motion studies and temporal action segmentation evaluations.

Constituency Lattice Encoding for Aspect Term Extraction (2020.coling-main)

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Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su

Syntax-aware Neural Semantic Role Labeling with Supertags (N19-1)

Challenge:	a challenge for aspect term extraction is to extract phrase-level aspect terms . a constituency lattice structure is constructed using the span annotations of constituents of a sentence .
Approach:	They propose to incorporate the span annotations of constituents of a sentence to leverage syntactic information in neural network models.
Outcome:	The proposed model outperforms existing models on two benchmark datasets.

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Jungo Kasai, Dan Friedman, Robert Frank, Dragomir Radev, Owen Rambow

Many Hands Make Light Work: Using Essay Traits to Automatically Score Essays (2022.naacl-main)

Challenge:	a new syntax-aware model for dependency-based semantic role labeling outperforms syntax-based models for English and Spanish.
Approach:	They propose a syntax-aware model for dependency-based semantic role labeling that outperforms syntax-based models for English and Spanish.
Outcome:	The proposed model outperforms syntax-agnostic models for English and Spanish.

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Rahul Kumar, Sandeep Mathias, Sriparna Saha, Pushpak Bhattacharyya

Toward Fast and Accurate Neural Discourse Segmentation (D18-1)

Challenge:	In automatic essay grading, essay traits are important for scoring the essay holistically . a single-task learning system gives the best results for scoring essays holistically and scoring essay traits.
Approach:	They propose a way to score essays using a multi-task learning approach . they compare the MTL-based BiLSTM system to a single-task Learning approach based on LSTMs and BiLStms .
Outcome:	The proposed system gives better results for scoring essay holistically and scoring essay traits.

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Yizhong Wang, Sujian Li, Jingfeng Yang

Discourse & Pragmatics

Challenge:	Existing discourse segmenters rely on complicated hand-crafted features and are not practical in actual use.
Approach:	They propose an end-to-end neural segmenter based on BiLSTM-CRF framework that can segment texts fast and accurately using a large corpus.
Outcome:	The proposed model is significantly faster than previous methods while achieving state-of-the-art performance on the RST-DT corpus.

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction (2021.acl-short)

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Quzhe Huang, Shengqi Zhu, Yansong Feng, Yuan Ye, Yuxuan Lai, Dongyan Zhao

DoLFIn: Distributions over Latent Features for Interpretability (2020.coling-main)

Challenge:	Document-level relation extraction (RE) is more challenging than sentence RE as it often requires reasoning over multiple sentences.
Approach:	They propose a method to heuristically select evidence sentences for document-level relation extraction.
Outcome:	The proposed method can be easily combined with BiLSTM to achieve good performance on benchmark datasets even better than fancy graph neural network based methods.

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Phong Le, Willem Zuidema

Explainability & Interpretability in NLP

Challenge:	Existing approaches to interpret neural networks face a trade-off between a model's usefulness and its complexity.
Approach:	They propose a novel approach to achieve interpretability that avoids this trade-off by using probability as the central quantity instead of a fixed quantity.
Outcome:	The proposed approach outperforms the classical CNN and BiLSTM classifiers on the SST2 and AG-news datasets.

Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach (2020.findings-emnlp)

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Marcos Lima, Roberta Silva, Felipe Lopes de Souza Mendes, Leonardo R. de Carvalho, Aleteia Araujo, Flavio de Barros Vidal

Efficient Convolutional Neural Networks for Diacritic Restoration (D19-1)

Challenge:	Public works procurements are a preferred field for collusion and fraud in Brazil . current methods of fraud detection use structured data to classification and usually do not involve annotated data.
Approach:	They propose to use public works procurements to classify risky entries using a dataset of 15,132,968 textual entries of which 1,907 are annotated.
Outcome:	The proposed datasets show that both bottleneck deep neural network and biLSTM are competitive compared with classical classifiers and achieve better precision (93.0% and 92.4%, respectively).

Somm: Into the Model (D18-1)

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Shengli Hu

Challenge:	sommeliers have three skills: wine theory, blind tasting, and beverage service . current study suggests that the sophist profession is at least to some extent automatable .
Approach:	They propose to train machine learning models that match sommelier's skills and compare results with real data.
Outcome:	The proposed models outperform human sommeliers on most tasks, compared with real data from a large group of wine professionals.

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Sawsan Alqahtani, Ajay Mishra, Mona Diab

Recursive Subtree Composition in LSTM-Based Dependency Parsing (N19-1)

Challenge:	Diacritic restoration is a computational task that requires a computer to understand written texts.
Approach:	They propose to use Temporal Convolutional Neural Networks (TCN) to restore missing diacritics for each character in written text.
Outcome:	The proposed model improves on TCN in Arabic, Yoruba, and Vietnamese.

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Miryam de Lhoneux, Miguel Ballesteros, Joakim Nivre

Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)

Challenge:	Existing studies show that tree structure modelling on top of sequence modelling is not feasible.
Approach:	They propose to recursively compose subtree representations in a biLSTM-based parser to capture subtreas.
Outcome:	The proposed model improves performance under ablating the backward LSTM and the forward LS.

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Yash Kankanampati, Joseph Le Roux, Nadi Tomeh, Dima Taji, Nizar Habash

Representation Learning

Challenge:	Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function.
Approach:	They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks.
Outcome:	The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems.

Negative Focus Detection via Contextual Attention Mechanism (D19-1)

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Longxiang Shen, Bowei Zou, Yu Hong, Guodong Zhou, Qiaoming Zhu, AiTi Aw

Challenge:	Negation is a universal but complicated linguistic phenomenon that reverses the polarity of a statement or its property into opposite.
Approach:	They propose a framework which consists of a Bidirectional Long Short-Term Memory neural network and a Conditional Random Fields layer to capture contextual information.
Outcome:	The proposed framework improves on the SEM’12 shared task corpus, yielding an absolute improvement of 2.11% over the state-of-the-art.

Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020.findings-emnlp)

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Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, Rui Xia

Pre- and In-Parsing Models for Neural Empty Category Detection (P18-1)

Challenge:	Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract aspect terms and opinion terms from review text in the form of opinion pairs or opinion triplets.
Approach:	They propose a grid-based AFOE tagging scheme to address the task in an end-to-end fashion only with one unified grid tracking task.
Outcome:	The proposed tagging scheme outperforms baselines and achieves state-of-the-art performance.

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Yufei Chen, Yuanyuan Zhao, Weiwei Sun, Xiaojun Wan

Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling (2022.acl-long)

Challenge:	Existing studies on empty category detection have shown positive effects on syntactic parsing . empty categories are used to indicate long-distance dependencies, discontinuous constituents, and certain dropped elements.
Approach:	They propose to use ECD to detect empty categories without syntactic analysis.
Outcome:	The proposed models outperform the prior state-of-the-art by significant margins.

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Elena Álvarez-Mellado, Constantine Lignos

Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (2020.coling-main)

Challenge:	a corpus of Spanish newswire rich in unassimilated lexical borrowings is used to identify the language of a word.
Approach:	They propose to annotate a corpus of Spanish newswire rich in unassimilated lexical borrowings and evaluate how models perform on this task.
Outcome:	The proposed model outperforms models fed with subword embeddings and Transformer-based embeddables on the Spanish newswire corpus.

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Huibin Ruan, Yu Hong, Yang Xu, Zhen Huang, Guodong Zhou, Min Zhang

Challenge:	Existing models for discourse relation recognition use self-attention and interactive-attention mechanisms.
Approach:	They develop a propagative attention learning model using a cross-coupled two-channel network.
Outcome:	The proposed model improves on the baseline models on a Penn Discourse Treebank.

Neural Transition Based Parsing of Web Queries: An Entity Based Approach (D18-1)

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Rivka Malca, Roi Reichart

Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge (2020.acl-main)

Challenge:	Existing query parsers that account for the unique grammar of web queries rely on resources not available outside of big web corporations.
Approach:	They propose a biLSTM query parser that explicitly accounts for the unique grammar of queries.
Outcome:	The proposed query parser outperforms existing state-of-the-art parsers on 2500 annotated queries.

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Bowen Zhang, Min Yang, Xutao Li, Yunming Ye, Xiaofei Xu, Kuai Dai

A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020.findings-emnlp)

Challenge:	Existing methods for stance detection are struggling to cope with the data across targets.
Approach:	They propose a model that uses external knowledge as a bridge to enable knowledge transfer across different targets.
Outcome:	The proposed model outperforms existing methods on a large real-world dataset.

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Chandan Akiti, Anna Squicciarini, Sarah Rajtmajer

Information Retrieval

Challenge:	Existing algorithms for self-disclosure identification and classification are challenging due to the relative anonymity of social networking sites and lack of non-verbal cues to signal thoughts or feelings.
Approach:	They propose an approach to detect emotional and informational self-disclosure in natural language by using frame semantics to identify lexical units and their semantic roles.
Outcome:	The proposed method improves on reddit data and provides insights into the drivers of disclosure behaviors.

Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features (2021.eacl-main)

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Thomas Haider

Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction (2020.lrec-1)

Challenge:	a prerequisite for the computational study of literature is the availability of properly digitized texts with reliable meta-data and ground-truth annotation.
Approach:	They propose to annotate prosodic features in large poetry corpora for English and German and train corpus driven neural models that enable large scale analysis.
Outcome:	The proposed models outperform baseline and BERT-based approaches in English and german and show that they learn foot boundaries better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit from each other.

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Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh, Reza Fahmi

Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations (2020.findings-emnlp)

Challenge:	Existing methods for emotion extraction and sentiment analysis produce invalid results due to the use of irony.
Approach:	They propose to use emoji prediction to fine tune a model using hand labeled tweets with irony tags.
Outcome:	The proposed method outperforms the state-of-the-art method on Persian dataset with an accuracy of 83.1% and offers strong baseline for further research in Persian language.

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Praveen Kumar Bodigutla, Aditya Tiwari, Spyros Matsoukas, Josep Valls-Vargas, Lazaros Polymenakos

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce generalizability of the trained models.
Approach:	They propose to use an adaptive multi-task loss function to minimize hand-crafted features to estimate user satisfaction at turn level from an end user perspective.
Outcome:	The proposed model improves on 28 Alexa domains, two dialogue systems and three user groups on a set of user-generated dialogues from 28 Alexia domain and 28 Alexis domains.

As easy as PIE: understanding when pruning causes language models to disagree (2025.findings-naacl)

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Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

On-device Structured and Context Partitioned Projection Networks (P19-1)

Challenge:	Language Model pruning reduces the model's efficiency by removing weights, nodes, or other parts of its architecture.
Approach:	They propose to prune Language Models (LMs) to produce smaller, hence more efficient models with small loss to their effectiveness.
Outcome:	The proposed pruning method hurts data points that matter the most when pruning . the proposed pruning technique is based on a new study of NLP datasets .

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Sujith Ravi, Zornitsa Kozareva

Challenge:	A challenge in on-device text classification is to build highly accurate models that fit in small memory footprint and have low latency.
Approach:	They propose an on-device neural network which learns compact projection vectors from raw text using structured and context-dependent partition projections.
Outcome:	The proposed model outperforms baseline models and surpasses RNN, CNN and BiLSTM models on dialog act and intent prediction.

Multi-granularity Textual Adversarial Attack with Behavior Cloning (2021.emnlp-main)

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Yangyi Chen, Jin Su, Wei Wei

Robustness in NLP

Challenge:	Existing adversarial attack models are vulnerable to adversarials crafted by human-imperceptible perturbations.
Approach:	They propose a multi-granularity adversarial attack model that generates high-quality adversarials with fewer queries to victim models.
Outcome:	The proposed model generates high-quality adversarial samples with fewer queries to victim models compared to baseline models . the proposed model also reduces query times for black-box models that only output labels without confidence scores .

Programming in Natural Language with fuSE: Synthesizing Methods from Spoken Utterances Using Deep Natural Language Understanding (2020.acl-main)

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Sebastian Weigelt, Vanessa Steurer, Tobias Hey, Walter F. Tichy

Multimodality

Challenge:	a new approach to teach new functions from natural language is needed to make intelligent systems programmable in everyday language.
Approach:	They propose to use natural language to teach intelligent systems new functions . fuSE synthesizes method signatures and API calls from spoken utterances .
Outcome:	The proposed system synthesizes 84.6% of method signatures and 79.2% of API calls correctly on unseen dataset.

Rethinking Complex Neural Network Architectures for Document Classification (N19-1)

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Ashutosh Adhikari, Achyudh Ram, Raphael Tang, Jimmy Lin

Information Retrieval

Challenge:	Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective .
Approach:	They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results .
Outcome:	a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons .

Incorporating Inner-word and Out-word Features for Mongolian Morphological Segmentation (2020.coling-main)

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Na Liu, Xiangdong Su, Haoran Zhang, Guanglai Gao, Feilong Bao

Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)

Challenge:	Mongolian morphological segmentation is a crucial preprocessing step in many Mongolian related NLP applications.
Approach:	They propose a neural network incorporating inner-word and out-word features for Mongolian morphological segmentation.
Outcome:	The proposed network is compared with baselines and evaluates its performance.

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Leyang Cui, Yue Zhang

Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

Challenge:	Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding.
Approach:	They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention.
Outcome:	The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model.

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Renjie Zhou, Zhongyi Xie, Jian Wan, Jilin Zhang, Yong Liao, Qiang Liu

Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System (2020.coling-main)

Challenge:	Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach:	They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome:	The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

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Hiroaki Takatsu, Ryota Ando, Yoichi Matsuyama, Tetsunori Kobayashi

Multimodality

Challenge:	In smart speakers and conversational robots, the demand for expressive speech synthesis has increased.
Approach:	They propose to annotate a news dataset with emotion labels for each sentence and to evaluate its effectiveness using the constructed dataset.
Outcome:	The proposed method improves the performance of the proposed model by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework.

Linguistically-Motivated Yorùbá-English Machine Translation (2022.coling-1)

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Ife Adebara, Muhammad Abdul-Mageed, Miikka Silfverberg

Multilinguality

Challenge:	Several phenomena where asymmetry arises have been identified as challenging problems for machine translation.
Approach:	They perform a fine-grained analysis of how an SMT system compares with two NMT systems when translating bare nouns into English.
Outcome:	The proposed model outperforms the SMT and BiLSTM models for 4 categories and the BiLST outperformed the SLT models for 3 categories.

Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)

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Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing

Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition (2022.lrec-1)

Challenge:	Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER.
Approach:	They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain.
Outcome:	The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively.

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Ali L. Hatab, Caroline Sabty, Slim Abdennadher

Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)

Challenge:	Word embeddings can capture the semantics of words and other hidden features, but the Arabic language is complex and requires a large amount of information to process.
Approach:	They propose to add morphological and syntactical features to Arabic word embeddings to train the model.
Outcome:	The proposed model outperforms the previous systems to the best of our knowledge.

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Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, Maosong Sun

Robustness in NLP

Challenge:	Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms.
Approach:	They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately.
Outcome:	The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods.

Deep Unknown Intent Detection with Margin Loss (P19-1)

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Ting-En Lin, Hua Xu

Rethinking Attribute Representation and Injection for Sentiment Classification (D19-1)

Challenge:	Existing methods for detecting unknown intents are difficult due to lack of examples.
Approach:	They propose a method for detecting unknown intents using bidirectional long-term memory networks with the margin loss as the feature extractor.
Outcome:	The proposed method can yield consistent improvements on two benchmark datasets.

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Reinald Kim Amplayo

Text Classification

Challenge:	Existing models that use text attributes to improve sentiment classification use text as a categorical feature.
Approach:	They propose to represent attributes as chunk-wise importance weight matrices and consider four locations to inject attributes.
Outcome:	The proposed method outperforms the state-of-the-art and outperformed previous models.

Embeddings for Named Entity Recognition in Geoscience Portuguese Literature (2020.lrec-1)

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Bernardo Consoli, Joaquim Santos, Diogo Gomes, Fabio Cordeiro, Renata Vieira, Viviane Moreira

Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)

Challenge:	Named Entity Recognition (NER) is a task within the field of Natural Language Processing that deals with the identification and categorization of Named entities (NEs) in a given text.
Approach:	They propose to use vector and tensor embeddings to train Portuguese Named Entity Recognition (NER) in the Geology domain.
Outcome:	The proposed model achieves state-of-the-art for the Portuguese Geology domain with one of its embeddings.

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Ying Luo, Hai Zhao

Structured Data in NLP

Challenge:	Existing models only consider the unidirectional delivery of information from innermost layers to outer ones, but instead focus on nested entities.
Approach:	They propose a bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER) the bipartites are bidirectional LSTM and graph convolutional network (GCN) they first use the entities recognized by the flat NER module to construct an entity graph .
Outcome:	The proposed model outperforms existing models on three standard nested NER datasets.

Dependency Parsing for Urdu: Resources, Conversions and Learning (2020.lrec-1)

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Toqeer Ehsan, Miriam Butt

Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings (2020.lrec-1)

Challenge:	Existing treebanks for Urdu are under-resourced due to lack of resources.
Approach:	They propose to convert existing treebanks into a common format that is based on Universal Dependencies.
Outcome:	The proposed format outperforms the MaltParser and a transition-based BiLSTM parser with word embeddings and significantly improves parsing accuracy.

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Yung Han Khoe

BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset (L18-1)

Challenge:	Reproducibility of research results is only recently beginning to be practiced and acknowledged . a research culture that focuses on beating previous benchmarks while disregarding the need to contribute to scientific knowledge and understanding is a problem, says a researcher.
Approach:	They reproduced work on morphosyntactic tagging using a meta-model . they did not contact the original authors for reproduction .
Outcome:	The proposed model outperforms previous models on morphological tagging tasks but fails to match the F1-scores reported for the meta-BiLSTM model.

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Hanieh Poostchi, Ehsan Zare Borzeshi, Massimo Piccardi

Afaan Oromo Hate Speech Detection and Classification on Social Media (2022.lrec-1)

Challenge:	Named-entity recognition (NER) is a natural language processing component that aims to identify all the "named entities" (NEs) in an unstructured text.
Approach:	They propose a deep learning approach for name-entity recognition in Persian . they publicize an entity-annotated Persian dataset and train word embeddings .
Outcome:	The proposed approach achieves a 77.45% CoNLL F 1 score for Persian NER based on a deep learning architecture and pre-trained word embeddings.

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Teshome Mulugeta Ababu, Michael Melese Woldeyohannis

Improving Neural Metaphor Detection with Visual Datasets (2020.lrec-1)

Challenge:	Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo.
Approach:	They develop a model to detect and classify Afaan Oromo hate speech on social media using different machine learning algorithms.
Outcome:	The proposed model outperforms existing models in gender, religion, race, and offensive speech on social media.

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Gitit Kehat, James Pustejovsky

Visual Data in NLP

Challenge:	a new method for metaphor detection uses text from visual datasets to identify words . a metaphor is a complex interaction between two terms, creating an "implicationcomplex"
Approach:	They propose a technique for sampling text from visual datasets to create a visibility word embedding.
Outcome:	The proposed method improves on previous approaches that use more complex neural networks and richer linguistic features for verb classification.

ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)

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Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, Maosong Sun

Robustness in NLP

Challenge:	Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness.
Approach:	They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks.
Outcome:	The proposed method can handle all the textual backdoor attack situations.

SEKE: Specialised Experts for Keyword Extraction (2025.findings-emnlp)

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Matej Martinc, Thi Hong Hanh Tran, Senja Pollak, Boshko Koloski

ManNER & ManPOS: Pioneering NLP for Endangered Manchu Language (2024.lrec-main)

Challenge:	Keyword extraction involves identifying the most descriptive words in a document . supervised keyword extraction is based on the mixture of experts (MoE) technique .
Approach:	They propose a supervised keyword extraction approach based on the mixture of experts technique . they use a learnable routing sub-network to direct information to specialised experts .
Outcome:	The proposed approach is based on the mixture of experts (MoE) technique . experts attend to each token and integrate it with a bidirectional long-term memory network .

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Sangah Lee, Sungjoo Byun, Jean Seo, Minha Kang