Papers with feature representations
Dual-Channel Span for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Existing approaches to extract sentiment triplets are too noisy and enumerate all possible spans. |
| Approach: | They propose a dual-channel span generation method to constrain the search space of span candidates. |
| Outcome: | The proposed method reduces span enumeration by nearly half on two versions of public datasets. |
A Simple and Effective Dependency Parser for Telugu (2020.acl-srw)
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| Challenge: | Existing dependency parsers for Telugu use hand-crafted features based on linguistic information like part-of-speech and morphology which are expensive to annotate. |
| Approach: | They propose to replace linguistic feature templates with a minimal feature function for Telugu . they train a BERT model on the Telugus Wikipedia data and use contextual vector representations to train the parser. |
| Outcome: | The proposed parser achieves state-of-the-art for Telugu using contextual vector representations . the proposed model trains on the Telugus Wikipedia data and trains with a greedy transition based approach . |
Deep Dirichlet Multinomial Regression (N18-1)
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| Challenge: | supervised topic models can incorporate arbitrary document-level features to inform topic priors, but their ability to model corpora is limited by the representation and selection of these features. |
| Approach: | They propose a generative topic model that simultaneously learns document feature representations and topics. |
| Outcome: | The proposed model outperforms DMR and LDA on three datasets and human subjects judge it more representative of associated document features. |
Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey (2025.emnlp-main)
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| Challenge: | Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes. |
| Approach: | They provide a review of controllable TTS methods from traditional control techniques to emerging approaches using natural language prompts. |
| Outcome: | The proposed methods are based on models, strategies, and features, and summarize challenges, datasets, and evaluations. |
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020.coling-main)
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| Challenge: | a novel context-aware dynamic convolution network is proposed to better leverage the local contexts when dynamically generating convolution kernels. |
| Approach: | They propose a dynamic convolution network to leverage local contexts when generating convolution kernels. |
| Outcome: | The proposed frameworks achieve state-of-the-art on two benchmark datasets. |
End-to-End Graph-Based TAG Parsing with Neural Networks (N18-1)
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| Challenge: | Using BiLSTMs, highway connections, and character-level CNNs, we propose a graph-based Tree Adjoining Grammar (TAG) parser. |
| Approach: | They propose a graph-based Tree Adjoining Grammar parser that uses BiLSTMs, highway connections, and character-level CNNs. |
| Outcome: | The proposed parser outperforms the previously reported best by more than 2.2 LAS and UAS points. |
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances (2025.findings-naacl)
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| Challenge: | Recent studies have demonstrated that Large Language Models possess a form of emotional intelligence, capable of interpreting emotional stimuli in text. |
| Approach: | They propose a method that translates speech characteristics into natural language descriptions and integrates them into LLMs to perform multimodal emotion analysis via text prompts. |
| Outcome: | The proposed method outperforms baseline models that require structural modifications on two datasets showing significant improvements in emotion recognition accuracy. |
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages (2020.coling-main)
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Diptesh Kanojia, Raj Dabre, Shubham Dewangan, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni
| Challenge: | a study of 14 Indian languages shows that cognates can be detected by word embeddings . cognates are variants of the same lexical form across languages . |
| Approach: | They propose to use cross-lingual word embeddings to detect cognates among 14 Indian languages . they then evaluate the impact of their method on neural machine translation . |
| Outcome: | The proposed method improves on a dataset of 12 Indian languages . it also improves quality of the extracted cognates by up to 2.76 BLEU . |
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling (2024.lrec-main)
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| Challenge: | Existing models lack feature representations that capture the deep semantics of language and sensitivity to minor input variations, resulting in significant changes in the generated text. |
| Approach: | They propose an end-to-end model architecture called ASEM that performs emotion analysis on top of sentiment analysis for open-domain chatbots. |
| Outcome: | The proposed model outperforms existing models for generating empathetic embeddings, providing e-mpathetic and diverse responses. |
Modality-specific Learning Rates for Effective Multimodal Additive Late-fusion (2022.findings-acl)
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| Challenge: | Multimodal machine learning uses additive late-fusion to combine feature representations from different modalities into a joint representation. |
| Approach: | They propose a Modality-Specific Learning Rate method to build late-fusion multimodal models from fine-tuned unimodal models. |
| Outcome: | The proposed method outperforms global learning rates on multiple tasks and settings and enables the models to effectively learn each modality. |
Exploring Logically Dependent Multi-task Learning with Causal Inference (2020.emnlp-main)
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| Challenge: | Hierarchical multi-task learning models can utilize task dependencies by stacking encoders and outperform democratic ones. |
| Approach: | They propose a model that utilizes the labels of all lower-level tasks and a Gumbel sampling model to deal with cascading errors. |
| Outcome: | The proposed model outperforms democratic models on six out of seven subtasks and achieves state-of-the-art on the two English and one Chinese datasets. |
CoVariance-based Causal Debiasing for Entity and Relation Extraction (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition and Relation Extraction are key tasks of Information Extraction. |
| Approach: | They propose a causal framework called c ovariance and variance optimization framework (OVO) to optimize feature representations and conduct general debiasing. |
| Outcome: | The proposed framework minimizes characterizing features’ covariance for alleviating selection and distribution bias and enhances feature representation in the feature space. |
Synonym relations affect object detection learned on vision-language data (2024.findings-naacl)
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| Challenge: | a recent study shows that vision-language models that accept textual input are not robust to variations in how input is provided. |
| Approach: | They propose two approaches to improve vision-language object detectors' performance . they use back-translation and class embedding enrichment to improve their models . |
| Outcome: | The proposed approaches improve performance on synonyms from mAP@0.3=33.87% to 37.93%. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings. |
| Approach: | They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment. |
| Outcome: | The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning . |
X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)
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| Challenge: | Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline . |
| Approach: | They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. |
| Outcome: | The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability. |
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)
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| Challenge: | Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks. |
| Approach: | They propose a parameter-free probing technique for analyzing pre-trained language models . their method does not require direct supervision from probing tasks . |
| Outcome: | The proposed method improves on linguistically-uninformed baselines on pre-trained language models. |
Large Language Models are Good Relational Learners (2025.acl-long)
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| Challenge: | Existing approaches to serialize large language models disregard critical relational structures and creates redundancies. |
| Approach: | They propose a graph neural network encoder to create structured relational prompts for large language models within a retrieval-augmented generation framework. |
| Outcome: | The proposed architecture preserves relational structure of databases while enabling LLMs to process and reason over complex entity relationships. |
Multimodal End-to-End Sparse Model for Emotion Recognition (2021.naacl-main)
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| Challenge: | Existing work in emotion recognition uses a two-phase pipeline, but the extracted features are fixed and cannot be fine-tuned on different tasks. |
| Approach: | They propose a two-phase pipeline for emotion recognition and personality recognition . they propose restructured datasets to enable fully end-to-end training . |
| Outcome: | The proposed model outperforms the current state-of-the-art models on emotion recognition and personality recognition tasks with half less computation in the feature extraction part. |
Just Rank: Rethinking Evaluation with Word and Sentence Similarities (2022.acl-long)
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| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
| Approach: | They propose a new intrinsic evaluation method called EvalRank which shows a much stronger correlation with downstream tasks. |
| Outcome: | The proposed method shows a much stronger correlation with downstream tasks and is released for future benchmarking purposes. |
PE: A Poincare Explanation Method for Fast Text Hierarchy Generation (2024.findings-emnlp)
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| Challenge: | Recent work on feature interactions neglects underlying linguistic information in feature representations. |
| Approach: | They propose a method for modeling feature interactions with hyperbolic spaces using Poincare Explanation. |
| Outcome: | The proposed method is able to model feature interactions with hyperbolic spaces in a time efficient manner. |
Selective Steering: Norm-Preserving Control Through Discriminative Layer Selection (2026.findings-acl)
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| Challenge: | Existing methods for inference-time steering are limited by their limitations . Angular Steering violates norm preservation, causing distribution shift and generation collapse . |
| Approach: | They propose a method that uses a norm-preserving rotation formulation to maintain activation distribution integrity and discriminative layer selection to apply steering only where features exhibit opposite-signed class alignment. |
| Outcome: | Experiments show that Selective Steering achieves higher attack success rates than prior methods while maintaining zero perplexity violations and approximately 100% capability retention on standard benchmarks. |
Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (2023.findings-acl)
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| Challenge: | Existing frameworks for detecting fake news videos are limited . a new approach is proposed to integrate neighborhood information of new videos . |
| Approach: | They propose a framework for automatically detecting fake news videos . it integrates neighborhood relationship of new videos belonging to same event . |
| Outcome: | The proposed framework improves performance of existing detectors and graph aggregation and debunking rectification modules. |
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (2020.acl-main)
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| Challenge: | Automated essay scoring (AES) can grade essays at scale, while automated writing evaluation (AWE) does not provide useful feature representations for supporting AWE. |
| Approach: | They propose a method for linking AWE and neural AES by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. |
| Outcome: | The proposed system is comparable to existing AWE systems for grading essays and representing essays as rubric-based features. |
DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis (2023.findings-acl)
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Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji
| Challenge: | a new task of conversational aspect-based sentiment analysis (DiaASQ) is designed to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. |
| Approach: | They propose a task of conversational aspect-based sentiment quadruple analysis to detect the quadrangle of target-aspect-opinion-sentiment in a dialogue. |
| Outcome: | The proposed task is based on a high-quality dataset in Chinese and English . it improves the end-to-end quadruple prediction and integrates rich feature representations . |
Latent Distribution Decouple for Uncertain-Aware Multimodal Multi-label Emotion Recognition (2025.findings-acl)
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Jingwang Huang, Jiang Zhong, Qin Lei, Gaojinpeng Gaojinpeng, Ymyang Ymyang, Sirui Wang, PeiguangLi PeiguangLi, Kaiwen Wei
| Challenge: | Existing studies focus on improving fusion strategies and modeling modality-to-label dependencies, but they overlook the impact of aleatoric uncertainty, which is inherent noise in multimodal data. |
| Approach: | They propose a latent emotional distribution decomposition with uncertainty perception framework to model aleatoric uncertainty in multimodal data. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the CMU-MOSEI and M3ED datasets, highlighting the importance of uncertainty modeling in MMER. |
Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |