Papers with GRU
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics (2021.findings-acl)
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| Challenge: | Existing models have limitations to generalize to diverse semantic phenomena, and it is unclear whether they can capture compositional meanings. |
| Approach: | They propose a systematic generalization testbed based on Natural language semantics to map natural language sentences to multiple meaning representations. |
| Outcome: | The proposed model can generalize to unseen combinations of quantifiers, negations, and modifiers, but not to the others. |
Neural News Recommendation with Long- and Short-term User Representations (P19-1)
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| Challenge: | Existing news recommendation methods learn a single representation for each user, which may be insufficient. |
| Approach: | They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder. |
| Outcome: | The proposed approach can learn both long- and short-term user representations on a real-world dataset. |
HiGRU: Hierarchical Gated Recurrent Units for Utterance-Level Emotion Recognition (N19-1)
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| Challenge: | Using textual features, our proposed HiGRU models achieve at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset. |
| Approach: | They propose a hierarchical gated recurrent unit framework to model word-level inputs and an upper-level GRU to capture contexts of utterance-level embeddings. |
| Outcome: | The proposed framework achieves 8.7%, 7.5%, 6.0% improvement over state-of-the-art methods on three datasets. |
Sentiment Analysis of Yelp Review Dataset: A Comparative Study of Machine Learning Methods (2026.acl-srw)
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| 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. |
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures (D19-1)
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| Challenge: | Traditionally, data-to-text applications have been designed using a modular pipeline architecture, in which the non-linguistic input data is converted into natural language through several intermediate transformations. |
| Approach: | They propose to use Gated-Recurrent Units and Transformer to implement neural pipelines for data-to-text generation. |
| Outcome: | The proposed models generalize better to unseen inputs and have better performance than the existing pipeline architectures. |
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters (N19-1)
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| Challenge: | Existing literature analysis does not focus on roles of characters or on relationships between them. |
| Approach: | They propose to combine emotion and character identification into a unified framework for character network extraction from fictional texts. |
| Outcome: | The proposed task is based on fan-fiction short stories and is able to predict emotion relations in the extracted network graph. |
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning (D18-1)
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| Challenge: | Pre-trained word embeddings provide significant improvements over untrained embeddables . Feature analysis reveals structural patterns of headline popularity . |
| Approach: | They use a multi-task GRU network to model headline popularity . they find that pre-trained word embeddings provide significant improvements over untrained embeddables . |
| Outcome: | The proposed model improves on pre-trained word embeddings and untrained embeddables . it also improves with the combination of two auxiliary tasks, news-section prediction and part-of-speech tagging . |
Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering (2021.findings-emnlp)
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| Challenge: | Document-level paraphrase generation is an important task in natural language processing. |
| Approach: | They propose a coherence relationship-guided paraphrase generation model that leverages graph GRU to encode the coherency relationship graph and get the cohesion-aware representation for each sentence. |
| Outcome: | The proposed model outperforms baseline models on BERTScore and diversity scores. |
On the Practical Computational Power of Finite Precision RNNs for Language Recognition (P18-2)
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| Challenge: | Recurrent Neural Networks (RNNs) are famously known to be Turing complete, but this relies on infinite precision in the states and unbounded computation time. |
| Approach: | They propose to use LSTM and Elman-RNN with ReLU activation to study RNNs . they show that LS and ReLU-RNns can easily implement counting behavior . |
| Outcome: | The LSTM and the Elman-RNN with ReLU activation are stronger than the RNN with squashing activation and the GRU. |
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU (2021.emnlp-main)
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| Challenge: | Existing approaches to combine HE and GC in RNNs suffer from long inference latency due to the slow activation functions. |
| Approach: | They propose a hybrid structure of HE and GC gated recurrent unit network, for low-latency secure inferences. |
| Outcome: | The proposed structure improves the secure inference latency by up to 138 over one of the state-of-the-art secure networks on the Penn Treebank dataset. |
Tweet Stance Detection Using an Attention based Neural Ensemble Model (N19-1)
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| Challenge: | Existing deep learning approaches to stance detection in twitter are inadequate to deal with the vanishing-gradient and overfitting problems. |
| Approach: | They propose a neural ensemble model that adopts strengths of two LSTM variants to learn better long-term dependencies. |
| Outcome: | The proposed model improves on the existing deep learning models on single and multi-target stance detection datasets. |
CEASE, a Corpus of Emotion Annotated Suicide notes in English (2020.lrec-1)
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| Challenge: | Existing studies on suicide notes have not explored the topic of emotion detection. |
| Approach: | They develop a fine-grained emotion annotated corpus of suicide notes in English and use it to perform emotion detection on a curated dataset. |
| Outcome: | The proposed model performs emotion detection on a curated dataset of 205 suicide notes in English. |
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction (2021.acl-long)
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| Challenge: | Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. |
| Approach: | They propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) which jointly learns how to understand language and correct spelling errors. |
| Outcome: | The proposed model outperforms state-of-the-art methods on widely used benchmarks and achieves superior performance against existing models. |
Adaptive Weighting for Neural Machine Translation (C18-1)
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| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
| Approach: | They propose adaptive weighting for WSMs to control the contribution of each input and output state. |
| Outcome: | The proposed weighting improves translation accuracy by 1.49 and 0.92 BLEU points on Chinese-to-English translation and English-to German translation tasks. |
FENAS: Flexible and Expressive Neural Architecture Search (2020.findings-emnlp)
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| Challenge: | Recent approaches to architecture search have shown good improvements in terms of performance with reasonable training speed. |
| Approach: | They propose an algorithm with more activation functions, input edges, and atomic operations to search for architectures that are optimal for given task. |
| Outcome: | The proposed algorithm reproduces well-known LSTM and GRU architectures and initializes with them for finding architectures more efficiently. |
A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text (L18-1)
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| Challenge: | a huge number of people use social media to express and exchange information in their own languages. |
| Approach: | They propose to use a code-mixed environment to extract higher level features from text . they use 'gadget' algorithm that automatically discovers higher level feature from text. |
| Outcome: | The proposed approach is generic and does not make use of handcrafted features or rules. |
Attending Sentences to detect Satirical Fake News (C18-1)
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| Challenge: | Existing approaches to capture news satire do not explore sentence and document difference . |
| Approach: | They propose a hierarchical deep neural network approach for satire detection . it is able to capture satirical news both at the sentence level and document level . |
| Outcome: | The proposed approach can capture satire at sentence and document levels. |
CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (N19-1)
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| Challenge: | Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. |
| Approach: | They propose to use a Chinese Named Entity Recognition (NER) model that uses a character-based convolutional neural network and a gated recurrent unit to capture the information from adjacent characters and sentence contexts. |
| Outcome: | The proposed model outperforms existing models on Weibo, MSRA and Chinese Resume datasets. |
The Lower The Simpler: Simplifying Hierarchical Recurrent Models (N19-1)
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| Challenge: | Using a simplified version of GRU, we replace the GRUs at the middle layers of hierarchical recurrent models with Fixed-size Ordinally-Forgetting Encoding (FOFE). |
| Approach: | They propose to make the lower layers simpler than the upper ones to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. |
| Outcome: | The proposed models contain less trainable parameters, consume less training time, and achieve slightly better performance than baseline models. |
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data (P19-1)
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| Challenge: | Existing QA methods lack scalability and performance is difficult to solve with document-level contexts. |
| Approach: | They propose an end-to-end deep network model that sequentially reads the input contexts into an external memory while replacing memories that are less important for answering unseen questions. |
| Outcome: | The proposed model improves on a synthetic dataset and real-world large-scale textual and video QA datasets. |
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks (D18-1)
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| Challenge: | Existing gated recurrent networks have a vanishing gradient, allowing for more matrix transformations and less transparent functions. |
| Approach: | They propose an additionsubtraction twin-gated recurrent network (ATR) to simplify neural machine translation. |
| Outcome: | The proposed system is more transparent than LSTM/GRU due to the simplification. |
Like a Baby: Visually Situated Neural Language Acquisition (P19-1)
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| Challenge: | A multi-modal neural architecture outperforms its equivalent trained on language alone with a 2% decrease in perplexity . |
| Approach: | They propose to use visual context to train neural language models to perform next-word prediction. |
| Outcome: | The proposed model outperforms its equivalent trained on language with 2% decrease in perplexity even when no visual context is available at test. |
The Learnability of Model-Theoretic Interpretation Functions in Artificial Neural Networks (2026.findings-acl)
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| Challenge: | Entity vectors improve scores on basic event, while gated architectures benefit most. |
| Approach: | They extend entity-level semantic representations, modern architectures, principled competing event generation, extended systematicity tests and a two-dimensional difficulty analysis disaggregating results by modifier complexity. |
| Outcome: | The proposed model-theoretic interpretation functions generalize systematically to out-of-training-sample sentences. |
A Sequential Flow Control Framework for Multi-hop Knowledge Base Question Answering (2022.emnlp-main)
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| Challenge: | Existing methods for multi-hop reasoning in knowledge base question answering are coarse-grained and may bring information loss. |
| Approach: | They propose a sequential reasoning self-attention mechanism to capture the crucial reasoning information of each hop in a more fine-grained way. |
| Outcome: | The proposed model achieves new state-of-the-art Hits@1 of 76.8% on WebQSP and is also effective when KB is incomplete. |
Unsupervised Opinion Summarisation in the Wasserstein Space (2022.emnlp-main)
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| Challenge: | Recent work on opinion summarisation of social media posts has focused on reviews . however, it is important to capture user opinions in online discussions over specific topics . |
| Approach: | They propose an unsupervised opinion summarisation model which uses the Wasserstein distance to generate a single summary from a group of documents. |
| Outcome: | The proposed model outperforms the state-of-the-art on ROUGE metrics and produces the best summaries with respect to meaning preservation according to human evaluations. |
Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (P19-1)
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| Challenge: | Numeracy is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Approach: | They propose to use a dataset to test whether neural network models can learn numeracy . numerability is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Outcome: | The proposed task can predict the magnitude of a numeral at a specific position in a text description. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
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| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
An LLM-Embedding Semantic Adaptation Network for Post-level Semantic Drift Evaluation (2026.findings-acl)
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| Challenge: | Evaluating semantic drift is essential for understanding discourse evolution and opinion formation in online discussions. |
| Approach: | They propose an LLM-embedding Semantic Adaptation Network to evaluate semantic drift . they use an LRU module, an LEM-Embedding graph convolutional network module and an adaptive fusion module to integrate features from event related posts. |
| Outcome: | The proposed model achieves state-of-the-art performance on the semantic drift evaluation task compared to baseline models. |
Transformers for Bridging Persian Dialects: Transliteration Model for Tajiki and Iranian Scripts (2024.lrec-main)
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| Challenge: | Despite its profound linguistic and cultural significance, Tajiki Persian remains a low-resource language with scant digitized datasets for computational applications. |
| Approach: | They propose to use Shahnameh, a seminal Persian epic poem, to train and assess Tajiki Persian transliteration models using two prominent sequence-to-sequence architectures: GRU with attention and transformer. |
| Outcome: | The proposed model outperforms pre-trained models with attention and transformer. |