Papers with neural architectures
Forecasting Firm Material Events from 8-K Reports (D19-51)
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| Challenge: | In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents of the company’s 8-K Current Reports. |
| Approach: | They exploit state-of-the-art neural architectures, including sequence-to-sequence architecture and attention mechanisms, to build a deep learning model that can forecast firm material event sequences based on company 8-K Current Reports. |
| Outcome: | The proposed model can forecast firm material event sequences based on the contents of the firm's 8-K Current Reports. |
Neural Architectures for Fine-Grained Propaganda Detection in News (D19-50)
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| Challenge: | MIC-CIS is a fine grained propaganda detection system . previous work focused on document level, labeling articles as propaganda . |
| Approach: | They propose to use different neural architectures to jointly perform propaganda detection tasks . they also investigate different ensemble schemes such as majority-voting, relax-vote, etc. |
| Outcome: | The proposed system performs sentences and fragment level propaganda detection tasks. |
How Important is a Language Model for Low-resource ASR? (2024.findings-acl)
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| Challenge: | Using an n-gram language model in ASR may seem obvious, but its absence in most implementations suggests otherwise. |
| Approach: | They examine whether using an n-gram language model in ASR can improve accuracy in low-resource languages. |
| Outcome: | The proposed model is absent in most implementations, but it does improve accuracy in English and Mandarin. |
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2021.acl-demo)
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
| Approach: | They propose an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of models, data, and evaluation metrics associated with text summarization. |
| Outcome: | The proposed tool can identify the shortcomings and failure modes of state-of-the-art summarization models. |
Compositional Generalization in Grounded Language Learning via Induced Model Sparsity (2022.naacl-srw)
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| Challenge: | induced model sparsity can help achieve compositional generalization and sample efficiency in grounded language learning problems. |
| Approach: | They propose a model that encourages sparse correlations between words and attributes to find a goal in a language-conditioned navigation problem with disentangled observations. |
| Outcome: | The proposed agent maintains high performance even when learning from a handful of demonstrations. |
Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages (D19-61)
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| Challenge: | Grapheme-to-phoneme conversion (g2p) is a task of predicting the pronunciation of words from their orthographic representation. |
| Approach: | They propose to leverage audio data as an auxiliary modality in a multi-task training process to learn a more optimal grapheme representation. |
| Outcome: | The proposed model reduces phoneme error rate to 2.46% on in-domain test set compared to unimodal spelling- pronunciation model. |
TMR: Evaluating NER Recall on Tough Mentions (2021.eacl-srw)
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| Challenge: | a NER evaluation tool is available via a repository. |
| Approach: | They propose to use Tough Mentions Recall to supplement traditional named entity recognition evaluation by examining recall on specific subsets of ”tough” mentions. |
| Outcome: | The proposed metrics enable differentiation between otherwise similar-scoring systems and identify patterns in performance that would go unnoticed from overall precision, recall, and F1. |
A Recurrent BERT-based Model for Question Generation (D19-58)
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| Challenge: | Existing QG models rely on recurrent neural networks (RNNs) but the inherent sequential nature of the RNN models suffers from the problem of handling long sequences. |
| Approach: | They propose to employ a pre-trained BERT language model to tackle question generation tasks. |
| Outcome: | The proposed model outperforms the existing models on the question-answering dataset SQuAD and advances the BLEU 4 score from 16.85 to 22.17. |
Extractive NarrativeQA with Heuristic Pre-Training (D19-58)
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| Challenge: | Automated question answering (QA) from text remains a challenge for humans . a striking gap exists between machine and human performance on NLP tasks . |
| Approach: | They propose a heuristic extractive version of a data set to solve the problem of answer extraction rather than generation. |
| Outcome: | The proposed model outperforms previous models on summary-level QA from full narratives and on the METEOR metric. |
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
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| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
| Outcome: | The proposed approach achieves state-of-the-art on three benchmark datasets. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
RegNLI: Detecting Online Product Misbranding through Legal and Linguistic Alignment (2026.eacl-industry)
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| Challenge: | Existing approaches to claim verification focus on keyword matching or generic text classification . misbranding involves deceptive labeling or advertising that misleads consumers about a product's nature or quality . |
| Approach: | They propose a framework that formulates misbranding detection as an inference task between product claims and regulatory provisions. |
| Outcome: | The proposed framework outperforms baselines in misbranding detection and regulation alignment metrics. |
Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation (N18-1)
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| Challenge: | Existing research lacks solid empirical investigation of typology of ad hominem arguments and their potential causes. |
| Approach: | They propose to perform several large-scale annotation studies and experiment with various neural architectures to validate hypotheses such as controversy or reasonableness. |
| Outcome: | The proposed model identifies the ad hominem fallacy and its possible causes using explainable neural network architectures. |
Examining the Inductive Bias of Neural Language Models with Artificial Languages (2021.acl-long)
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| Challenge: | a novel method for investigating inductive biases of language models using artificial languages is proposed . we show that modern neural architectures used for language modeling are intrinsically black boxes . |
| Approach: | They propose a method to investigate inductive biases of language models using artificial languages . they use languages to create parallel corpora across languages that differ only in word order . |
| Outcome: | The proposed method shows that language models can be used to model a wide variety of languages. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
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| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
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| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
Deep Neural Models for Medical Concept Normalization in User-Generated Texts (P19-2)
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| Challenge: | a medical concept normalization problem is a challenge since social media texts are ambiguous and noisy . a recent study shows that neural architectures leverage the semantic meaning of the entity mention . |
| Approach: | They propose to map a health-related entity mention to a controlled vocabulary . they use powerful neural networks and contextualized word representation models . |
| Outcome: | The proposed model outperforms existing state-of-the-art models in mapping medical concepts to medical terms . the proposed model is based on recurrent neural networks and contextualized word representation models . |
Syntactically Aware Neural Architectures for Definition Extraction (N18-2)
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| Challenge: | Existing approaches to identify definitional knowledge in text corpora are based on Wikipedia-like definitions. |
| Approach: | They propose to combine Convolutional and Recurrent Neural Networks to train definitional knowledge in text corpora. |
| Outcome: | The proposed models can be applied to more noisy domain-specific corpora. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
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| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
Unit Testing for Concepts in Neural Networks (2022.tacl-1)
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| Challenge: | Existing theories of language and cognition hold that these representations are structured in a compositional way and that the meanings of composite concepts (''gray car'') are inherited predictably from the meaning of the parts. |
| Approach: | They propose to test models for determining whether a system’s behavior is consistent with several key aspects of Fodor’s criteria. |
| Outcome: | The proposed models succeed on tests of groundedness, modularity, and reusability of concepts, but important questions about causality remain open. |
A Dataset for Document Grounded Conversations (D18-1)
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| Challenge: | a dataset of document grounded conversations provides information on content of a document . current datasets lacking conversation grounding do not provide this information . |
| Approach: | They propose a document grounded dataset for conversations . they use Wikipedia articles about popular movies to define document grounded conversations based on their results . |
| Outcome: | The proposed dataset provides a source of information and provides benchmark performance on the task of generating the next response. |
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020.acl-main)
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| Challenge: | Hate Speech (HS) is a pervasive issue that spreads quickly and widely . research has focused on avoiding undesired effects that come with content moderation . |
| Approach: | They propose to use large scale unsupervised language models to generate responses to hate effectively using large scale models. |
| Outcome: | The proposed methods lack quality data and produce generic/repetitive responses. |
Low-Resource Counterspeech Generation for Indic Languages: The Case of Bengali and Hindi (2024.findings-eacl)
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| Challenge: | NLP is a technique that generates counterspeech that “counters” the vicious tone of online abuse and dilutes/ameliorates their rippling effect over the social network. |
| Approach: | They propose to use neural architectures to generate counterspeech that can "counter" the vicious tone of online abuse and dilute/ameliorate their rippling effect over the social network. |
| Outcome: | The proposed model can generate counterspeech in monolingual setups and is more transferable when languages belong to the same language family. |
Can AMR Assist Legal and Logical Reasoning? (2022.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) has been shown to be useful for many downstream tasks. |
| Approach: | They propose neural architectures that utilize linearised AMR graphs in combination with pre-trained language models to capture logical relationships on multiple choice question answering tasks. |
| Outcome: | The proposed models outperform text-only baselines but outperformed text models, suggesting complementary abilities. |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
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| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
| Outcome: | The proposed model outperforms pipeline approaches in aspects-based sentiment analysis. |
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
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| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
Rational Recurrences (D18-1)
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| Challenge: | Recent studies show that neural models lack strong intuitions . recent studies show connections between convolutional neural networks and weighted finite state automata (WFSAs) |
| Approach: | They show that some recurrent neural networks share a connection to weighted finite state automata (WFSAs) they define rational recurrences as recursive hidden state update functions . they propose to use these functions to write forward calculations of a finite set of WFSA's . |
| Outcome: | The proposed model outperforms two baselines on language modeling and text classification. |
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks (2021.acl-long)
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| Challenge: | Existing models of NLP are fading away, but new ones are needed to maintain their dominance. |
| Approach: | They propose a method to pretrain a CNN using Wikipedia data and integrate it with standard TLMs. |
| Outcome: | The proposed method outperforms the original ALBERT on GLUE tasks and achieves similar performance to SOTA on open-domain QA tasks. |
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)
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| Challenge: | Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature. |
| Approach: | They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains. |
| Outcome: | The proposed approach is an adaptation process for masked LMs, not memory or components. |
Robust and Interpretable Grounding of Spatial References with Relation Networks (2020.findings-emnlp)
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| Challenge: | Existing models for understanding spatial references in text are vulnerable to noise in input text or state observations. |
| Approach: | They propose a text-conditioned relation network with a cross-modal attention module to capture fine-grained spatial relations between entities and a model that is robust and interpretable. |
| Outcome: | The proposed model improves performance on three tasks with a 17% improvement in predicting goal locations and a 15% improvement in robustness compared to state-of-the-art systems. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
Image-Chat: Engaging Grounded Conversations (2020.acl-main)
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| Challenge: | In order for machines to communicate with humans, they must understand the natural things that humans say about the world they live in and respond in kind. |
| Approach: | They propose to fuse a set of neural architectures using image and text representations to achieve this goal. |
| Outcome: | The proposed model performs well on the Image-Chat task and humans prefer it 47.7% of the time. |
Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension (D18-1)
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| Challenge: | Sequence encoders are crucial components in many neural architectures for learning to read and comprehend. |
| Approach: | They propose a compositional encoder that explicitly models across multiple granularities using a new dilated composition mechanism. |
| Outcome: | The proposed encoder is fast and expressive, and can model across multiple granularities. |
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning (D19-1)
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| Challenge: | Existing reading comprehension datasets focus on factual and literal understanding of context paragraphs, but our dataset focuses on reading between the lines over a diverse collection of everyday narratives. |
| Approach: | They propose a large-scale dataset that requires commonsense-based reading comprehension, formulated as multiple-choice questions. |
| Outcome: | The proposed architecture improves over the baselines of existing reading comprehension datasets and shows a significant gap between machine (68.4%) and human performance (94%). |
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory (2022.coling-1)
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| Challenge: | Essay exams have two drawbacks in that grading them is expensive and raises questions about fairness. |
| Approach: | They propose to use a multidimensional item response theory model to improve interpretability while maintaining scoring accuracy. |
| Outcome: | The proposed model improves interpretability while maintaining accuracy while preserving cost and accuracy. |
Character-Level Feature Extraction with Densely Connected Networks (C18-1)
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| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
GraphMR: Graph Neural Network for Mathematical Reasoning (2021.emnlp-main)
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| Challenge: | Existing studies have shown the effectiveness of sequence-to-sequence (Seq2Seque) on mathematics solving. |
| Approach: | They propose a graph-to-sequence neural network which can learn hierarchical information of graphs inputs to solve mathematical problems and speculate answers. |
| Outcome: | The proposed neural network outperforms other neural networks in hidden information learning and mathematics resolving. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
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| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention (2020.coling-main)
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| Challenge: | SANs are an integral part of successful neural networks such as Transformer . training SAN on a task or pretraining them on language modeling requires large amounts of data and compute resources. |
| Approach: | They propose to modify SANs to enable faster learning, i.e., higher accuracies after fewer update steps. |
| Outcome: | The proposed modifications enable faster learning, i.e., higher accuracies after fewer update steps. |
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation (2024.acl-long)
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| Challenge: | Popular neural architectures lack strong structural inductive biases for seq2seq NLP tasks . previous work shows that these models struggle with systematic generalization . |
| Approach: | They propose to inject a structural inductive bias into a seq2seq model by pre-training it to simulate structural transformations on synthetic data. |
| Outcome: | The proposed method improves few-shot learning and generalization of FST-like models. |
NLP Service APIs and Models for Efficient Registration of New Clients (2020.findings-emnlp)
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| Challenge: | State-of-the-art NLP inference uses enormous neural architectures and models trained for GPU-months, well beyond the reach of most consumers of NLP. |
| Approach: | They propose a centralized NLP service that can be customized to suit clients . they propose NER, sentiment labeling, and predictive language modeling to improve client experience. |
| Outcome: | The proposed model can be used to improve word usage and salience across clients without re-training or fine-tuning. |
New Dataset and Strong Baselines for the Grammatical Error Correction of Russian (2021.findings-acl)
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| Challenge: | a new resource is created to evaluate grammatical error correction models in English . a subset of the dataset is annotated in Russian, which is hard to come by and expensive to annotate . |
| Approach: | They develop an annotated learner corpus of Russian extracted from the Lang-8 website. |
| Outcome: | The proposed dataset is compared against two state-of-the-art grammatical error correction models . the results show that the created corpus is more diverse than the existing one . |
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection (P19-1)
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| Challenge: | linguistics and cognitive science have long debated the cognitive mechanisms needed to account for the English past tense. |
| Approach: | They propose to use an encoder-decoder model to account for the english past tense . they also show that ED models demonstrate humanlike performance in a nonce-word task . |
| Outcome: | The proposed model is unstable across simulations and does not fit to human data . other neural models might do better, but there is insufficient evidence to claim them . |
Training Language Models with Memory Augmentation (2022.emnlp-main)
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| Challenge: | Existing methods for training memory-augmented language models only introduce mem-ories at testing time or represent them using a separately trained encoder. |
| Approach: | They propose a training approach that directly takes in-batch examples as accessible memory and new methods for memory construction and data batching that are used for adapting to different sets of memories at testing time. |
| Outcome: | The proposed approach reduces perplexity from 18.70 to 15.37 on multiple language modeling and machine translation benchmarks. |
Dissecting Span Identification Tasks with Performance Prediction (2020.emnlp-main)
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| Challenge: | Span identification tasks are a staple of applied NLP, but there is little insight on how their properties influence their difficulty. |
| Approach: | They propose to build a model to predict span ID performance for unseen span ID tasks that can support architecture choices. |
| Outcome: | The proposed model predicts span ID tasks for unseen span ID task in English, and the meta model predictable span ID performance. |
Revisiting Simple Neural Probabilistic Language Models (2021.naacl-main)
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| Challenge: | Recent advances in language modeling have been driven not only by advances in neural architectures, but also through hardware and optimization improvements. |
| Approach: | They revisit the neural probabilistic language model (NPLM) of Bengio et al. (2003) which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. |
| Outcome: | The proposed model performs better on word-level language model benchmarks than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. |
Equivariant Transduction through Invariant Alignment (2022.coling-1)
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| Challenge: | Existing studies have demonstrated that NLP models possess the ability to generalize compositionally, but none have tested it. |
| Approach: | They propose to use a group-equivariant neural network to encode an inductive bias for SCAN to test for this ability. |
| Outcome: | The proposed architecture outperforms existing group-equivariant approaches on the SCAN task and shows that it can generalize compositionally. |
Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art (2024.acl-long)
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| Challenge: | Automated essay scoring (AES) is a task of assigning a single score to an essay . authors abandon sophisticated neural architectures and develop a simple feature-based approach . |
| Approach: | a team of researchers develop a feature-based approach to cross-prompt automated essay scoring that adopts a simple neural architecture. |
| Outcome: | a new approach to cross-prompt automated essay scoring can achieve state-of-the-art results. |
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines (N19-1)
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| Challenge: | Experimental results show that standalone n-gram models lend themselves as natural choices for resource-lean or morphologically rich languages. |
| Approach: | They run experiments on 50 languages covering all morphological language families to compare n-gram models with lstm models. |
| Outcome: | The proposed extension outperforms an lstm language model on 42 languages while its extension which explicitly injects linguistic knowledge outperformed the character-aware neural model on 8 languages. |
Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |
Learning to Deceive with Attention-Based Explanations (2020.acl-main)
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| Challenge: | Attention mechanisms are ubiquitous components in neural network architectures and are often claimed to confer interpretability. |
| Approach: | They propose a method for training models to produce deceptive attention masks by combining weights assigned to designated impermissible tokens with a weighted sum. |
| Outcome: | The proposed method reduces the weight assigned to designated impermissible tokens while still using them across multiple models and tasks. |
TransQuest: Translation Quality Estimation with Cross-lingual Transformers (2020.coling-main)
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| Challenge: | Recent advances in the field of sentence-level quality estimation (QE) are based on neural-based architectures that require resourceintensive training. |
| Approach: | They propose a framework for sentence-level quality estimation based on cross-lingual transformers and use it to implement and evaluate two different neural architectures. |
| Outcome: | The proposed framework outperforms open-source QE frameworks when trained on WMT datasets and is very competitive in transfer learning settings. |
More Diverse Dialogue Datasets via Diversity-Informed Data Collection (2020.acl-main)
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| Challenge: | Existing approaches to generate conversational dialogue produce uninteresting, predictable responses. |
| Approach: | They propose a method to collect and determine more diverse data from conversational participants . they use dynamically computed corpus-level statistics to determine which conversational participant to collect data from . |
| Outcome: | The proposed method produces significantly more diverse data than baseline methods and better results on emotion classification and dialogue generation tasks. |
A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings (2020.coling-main)
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| Challenge: | Existing methods for learning entity embeddings from text descriptions leave it to downstream applications to identify these different facets and to select the most relevant ones. |
| Approach: | They propose a model that instead learns several vectors for each entity, each of which captures a different aspect of the considered domain. |
| Outcome: | The proposed model learns several vectors for each entity, each of which intuitively captures a different aspect of the considered domain. |
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)
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| Challenge: | Existing neural IR models do not have a mechanism for treating expansion terms differently from the original query terms, making it difficult to combine them with existing PRF approaches. |
| Approach: | They propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. |
| Outcome: | Extensive experiments on two standard test collections confirm the effectiveness of the proposed framework in improving the performance of two state-of-the-art neural IR models. |
LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)
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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li
| Challenge: | Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). |
| Approach: | They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning. |
| Outcome: | The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks. |
Open-domain Video Commentary Generation (2022.emnlp-main)
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Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura
| Challenge: | Existing approaches to generate live commentary on specific domains have been limited. |
| Approach: | They propose to generate live commentary from transcribed videos in an open-domain setting . they propose to use well-known neural architectures to build models based on transcriptions . |
| Outcome: | The proposed model is based on well-known neural architectures and based off existing models. |
Understanding How Value Neurons Shape the Generation of Specified Values in LLMs (2025.findings-emnlp)
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| Challenge: | Current approaches to interpret value representations are limited by superficial judgments over mechanistic analysis. |
| Approach: | They propose a mechanistic interpretability framework that uses the Schwartz Values Survey to interpret value . they use a dataset that operationalizes four dimensions of universal value through behavioral contexts . |
| Outcome: | The proposed method bridges psychological value frameworks with neuron analysis in large language models. |
On the Benefit of Syntactic Supervision for Cross-lingual Transfer in Semantic Role Labeling (2021.emnlp-main)
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| Challenge: | Recent advances in neural architectures and pre-trained representations have greatly improved the performance of fully-supervised semantic role labeling (SRL) but there are limitations in the availability of supervised training data. |
| Approach: | They propose to leverage syntactic dependencies to facilitate cross-lingual transfer by annotating predicate-argument structures in text. |
| Outcome: | The proposed model can be extended to other languages with limited training data. |
Tribrid: Stance Classification with Neural Inconsistency Detection (2021.emnlp-main)
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| Challenge: | a new neural architecture can be used to classify stances on social media without relying on linguistic features. |
| Approach: | They propose a neural architecture where the input also includes automatically generated negated perspectives over a given claim. |
| Outcome: | The proposed model improves on the original input and removes doubtful predictions over the retained information. |
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference (2020.acl-main)
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| Challenge: | Using custom architectures, constituency parsers are limited and require specialized hardware. |
| Approach: | They propose an algorithm that assigns labels to each word in a sentence in parallel and then performs a reconciliation phase to extract a tree in (empirically) linear time. |
| Outcome: | The proposed model achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies. |
Working Hard or Hardly Working: Challenges of Integrating Typology into Neural Dependency Parsers (D19-1)
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| Challenge: | linguistic typology has shown great promise in pre-neural parsing, but results for neural architectures have been mixed. |
| Approach: | They explore the task of leveraging typology in the context of cross-lingual dependency parsing. |
| Outcome: | The proposed approach improves performance in the context of cross-lingual dependency parsing. |
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models (2020.coling-main)
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| Challenge: | a large number of biomedical entity mentions are retrieved from different ontologies, requiring non-syntactic interpretation. |
| Approach: | They propose to use bidirectional encoder representations from transformers to link biomedical entities across three domains for a task called medical concept normalization. |
| Outcome: | The proposed neural architectures are efficient for linking biomedical entities across domains and corpora. |
Neural Language Modeling for Named Entity Recognition (2020.coling-main)
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| Challenge: | Experimental results show that named entity recognition systems are faster and more flexible for the size of the corpus. |
| Approach: | They propose to use a neural language model as an alternative to the conditional random field layer for named entity recognition. |
| Outcome: | The proposed system has a significant speed advantage with a marginal performance degradation. |
From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)? (2020.acl-main)
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| Challenge: | a decade has passed since the establishment of SPMRL to address the peculiar challenges of Statistical Parsing for Morphologically-rich languages (MRLs). |
| Approach: | They propose a framework for parsing MRLs and propose implementing symbolic ideas into modern neural architectures. |
| Outcome: | The proposed strategies are based on the multi-tagging task in Hebrew, a morphologically-rich, high-fusion, language. |
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification (2021.emnlp-main)
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Daria Pylypenko, Kwabena Amponsah-Kaakyire, Koel Dutta Chowdhury, Josef van Genabith, Cristina España-Bonet
| Challenge: | Traditional hand-crafted features have been used for distinguishing between translated and original non-translated texts. |
| Approach: | They compare a feature-engineering-based approach to a features-learning-based one and use pre-trained neural word embeddings to train neural architectures. |
| Outcome: | The proposed approach outperforms other approaches by more than 20 accuracy points and the BERT-based model performs the best in both monolingual and multilingual settings. |
It’s High Time: A Survey of Temporal Question Answering (2026.acl-long)
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| Challenge: | Temporal Question Answering (TQA) is a research area that focuses on answering questions involving temporal constraints or context. |
| Approach: | They present a comprehensive overview of Temporal Question Answering (TQA) this research area focuses on answering questions involving temporal constraints or context . |
| Outcome: | The proposed frameworks are compared against a range of datasets, tasks, and approaches. |
Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute Scaling (2026.findings-acl)
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Ivan Rodkin, Daniil Orel, Konstantin Smirnov, Arman Bolatov, Bilal Elbouardi, Besher Hassan, Yuri Kuratov, Aydar Bulatov, Preslav Nakov, Timothy Baldwin, Artem Shelmanov, Mikhail Burtsev
| Challenge: | Reasoning is a core capability of large language models, yet how multi-step reasoning is learned and executed remains unclear. |
| Approach: | They evaluate how large language models learn multi-step reasoning without memorization . they find that most neural architectures trained from scratch can learn rule inference . |
| Outcome: | The proposed framework fails to solve a natural-language proxy task with high accuracy. |