Papers with joint modeling
Joint models for NLP (D18-3)
Copied to clipboard
| Challenge: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
| Approach: | This tutorial reviews main approaches to joint modeling for both statistical and neural methods. |
| Outcome: | This tutorial reviews main approaches to joint modeling for statistical and neural methods. |
Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
Copied to clipboard
| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
| Outcome: | The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages. |
AMR Parsing as Graph Prediction with Latent Alignment (P18-1)
Copied to clipboard
| Challenge: | Abstract meaning representations (AMRs) are sentence-level semantic representations . lack of explicit alignments between nodes in graphs and words in sentences is a challenge . |
| Approach: | They propose a neural parser which treats alignments as latent variables within a joint probabilistic model of concepts, relations and alignments. |
| Outcome: | The proposed parser achieves the best reported results on the standard benchmark (74.4% on LDC2016E25). |
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)
Copied to clipboard
| Challenge: | In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. |
| Approach: | They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks. |
| Outcome: | The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks. |
“Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space (2021.eacl-main)
Copied to clipboard
| Challenge: | Detecting arguments in online interactions is useful to understand how conflicts arise and get resolved. |
| Approach: | They propose to use a corpus annotated with argumentative moves and sarcasm to model sarcastic relationships using deep learning architectures. |
| Outcome: | The proposed setup improves the argumentative relation classification task using deep learning architectures. |
Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods of implicit discourse relation recognition (IDRR) focus on three aspects: enhancing discourse units representation, enhancing semantic interaction, and joint learning with other tasks. |
| Approach: | They propose a joint model to recognize the relation label and generate the target sentence containing the meaning of relations simultaneously. |
| Outcome: | The proposed model achieves the best performance against several state-of-the-art systems on Chinese and English datasets. |
A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for disease recognition and normalization ignore text surface form of each candidate concept, causing boundary inconsistency. |
| Approach: | They propose a neural transition-based joint model to normalize disease entities from biomedical text. |
| Outcome: | The proposed model improves on two publicly available datasets. |
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for vision-language pre-training can only learn from aligned image-caption data and rely heavily on expensive regional features. |
| Approach: | They propose an end-to-end unified-modal pre-training framework for joint learning . they propose to conduct grounded learning on both images and texts via a sharing grounded space . |
| Outcome: | The proposed model improves visual and visual semantic alignment on images and texts. |
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)
Copied to clipboard
| Challenge: | a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs . |
| Approach: | They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme. |
| Outcome: | The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws . |
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work on pragmatic reasoning tests using simple word reference games with unidentified speakers and listeners, but speakers' sociocultural background shapes their pragmatic assumptions. |
| Approach: | They propose a dataset which operationalizes sociocultural pragmatic inference in a word reference game. |
| Outcome: | The proposed model improves clue-giving and guessing tasks by accounting for background characteristics and the game context. |
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT (2020.acl-main)
Copied to clipboard
| Challenge: | Zero pronoun recovery and resolution aim at recovering the dropped pronounce and pointing out its anaphoric mentions. |
| Approach: | They propose to solve two tasks together to recover the dropped pronoun and point out its anaphoric mentions. |
| Outcome: | The proposed model outperforms previous state of the arts benchmarks on two benchmarks. |
Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Question Answering models typically use retrieval and reasoning components to identify relevant information for reasoning. |
| Approach: | They propose a retrieval parameterization method that marginalizes unanswerable queries . they show that marginalization allows a model to mitigate false negatives in annotations . |
| Outcome: | The proposed model improves on two multi-document question answering datasets and shows that marginalization improves performance. |
Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance (2025.emnlp-main)
Copied to clipboard
| Challenge: | Extensive experiments demonstrate that our approach significantly alleviates task interference and forgetting. |
| Approach: | They propose a framework for supervised fine-tuning for large language models . they first fine-tail the model on each task to identify its core parameter regions . |
| Outcome: | The proposed framework outperforms vanilla fine-tuning and baselines on multiple public benchmarks on reasoning, dialogue, instruction following, and more. |
Grounding Characters and Places in Narrative Text (2023.acl-long)
Copied to clipboard
| Challenge: | Prior work has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. |
| Approach: | They propose a task to assign a spatial relationship category for every character and location co-mention within a window of text, taking into account linguistic context, narrative tense, and temporal scope. |
| Outcome: | The proposed model allows to test hypotheses on mobility and domestic space . women as characters tend to occupy more interior space than men, the model shows . |
SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a new corpus of news media and articles is developed to assess political bias and factuality in cross-lingual contexts . integrity and objectivity of news are crucial in an age of information sharing across cultural and language landscapes - a recent study shows . |
| Approach: | They propose a corpus of news media and articles for predicting political bias and factuality . they evaluate the cross-lingual ability of the models; however, they evaluate on English data . |
| Outcome: | The proposed corpus is unprecedented in its collection and evaluates on English data. |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
Copied to clipboard
| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
| Approach: | They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase . |
| Outcome: | The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm . |
A Multitask Learning Approach for Diacritic Restoration (2020.acl-main)
Copied to clipboard
| Challenge: | Diacritics are used to specify pronunciations and meanings in many languages like Arabic. |
| Approach: | They propose to use multi-task learning to optimize diacritic restoration with related NLP problems . they use Arabic as a case study since it has sufficient data resources for tasks . |
| Outcome: | The proposed model outperforms baseline models and is comparable to the state-of-the-art models. |
Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging (2020.acl-main)
Copied to clipboard
| Challenge: | a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. |
| Approach: | They propose to model morphological features jointly, whether lexicalized or non-lexicalised . their results are compared to Arabic and Egyptian Arabic . |
| Outcome: | The proposed model achieves 20% relative error reduction in Arabic and 11% in Egyptian Arabic. |
Discriminative Reasoning with Sparse Event Representation for Document-level Event-Event Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level Event Causality Identification (DECI) is a sentence-level task that requires long-text understanding. |
| Approach: | They propose a document-level event causality identification model (SENDIR) that uses sparse attention to capture long-distance dependence. |
| Outcome: | The proposed model can be used to discriminate between event pairs in the same sentence or span multiple sentences. |
ViPE: Visual Perception in Parameter Space for Efficient Video-Language Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing video-language models rely on concatenating visual tokens with textual inputs for joint modeling, but this method suffers from significant inefficiency when scaling to long videos with dense visual inputs. |
| Approach: | They propose a video-to-parameter efficiency paradigm called ViPE that transforms video content into visual perceptual weights, which are directly injected into the LLM’s parameters. |
| Outcome: | The proposed model reduces FLOPs by 85% and inference time by up to 65% while reducing FLOP and FLOP inference times by up-to-65%. |