Partners in Crime: Multi-view Sequential Inference for Movie Understanding (D19-1)
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| Challenge: | Existing multi-view learning approaches are tested in unsupervised setups, allowing for learning of representation for monolithic data points, not sequences. |
| Approach: | They propose a neural architecture paired with a novel objective for incremental inference that integrates multi-view information for sequence prediction problems. |
| Outcome: | The proposed model outperforms previous work and strong baselines on two crime cases and speaker type tagging tasks that contribute to movie understanding. |
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| Challenge: | Recent work in vision-and-language pretraining has investigated supervised signals from object detection data to learn better, fine-grained multimodal representations. |
| Approach: | They propose two approaches to contextualise visual entities in a multimodal setup by using verbalised scene graphs and masked relation prediction. |
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Multi-view Story Characterization from Movie Plot Synopses and Reviews (2020.emnlp-main)
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| Challenge: | Existing methods for characterizing stories by generating tags from synopses suffer from coverage issues. |
| Approach: | They propose to use synopses and reviews to characterize stories by inferring attributes such as theme and style from written synopsis and reviews. |
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Multi-view and Cross-view Brain Decoding (2022.coling-1)
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| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
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M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)
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| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
| Approach: | They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions. |
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks (2026.findings-acl)
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| Challenge: | Survey aims to identify challenges of multimodal unlearning for vision, language, audio and video . retraining after deletion requests or policy updates is often impractical, survey finds . |
| Approach: | They propose to enable selective removal across modalities while retaining overall utility. |
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Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
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Bazinga! A Dataset for Multi-Party Dialogues Structuring (2022.lrec-1)
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Paul Lerner, Juliette Bergoënd, Camille Guinaudeau, Hervé Bredin, Benjamin Maurice, Sharleyne Lefevre, Martin Bouteiller, Aman Berhe, Léo Galmant, Ruiqing Yin, Claude Barras
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Tutorial on Multimodal Machine Learning (2022.naacl-tutorials)
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| Challenge: | Multimodal machine learning is a challenging but crucial area with numerous applications in multimedia, affective computing, robotics, finance, HCI, and healthcare. |
| Approach: | This tutorial will describe an updated taxonomy on multimodal machine learning synthesizing its core technical challenges and major directions for future research. |
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Multi-component Causal Tracing in Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) are prone to various forms of safety risks, such as learning and propagating societal biases and even creating harmful or deceptive content through jailbreak attacks. |
| Approach: | They propose a framework for causally tracing multiple components simultaneously that systematically identifies the subsets of components most critical to a desired performance metric. |
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A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis (2022.lrec-1)
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| Challenge: | Graph databases are well-suited for crossreferencing information from multiple sources to support machine learning tasks. |
| Approach: | They propose a graph-based structure of multiple resources enriched with graph analytics approaches to provide an encompassing view of the movie recommendation domain and of the way people talk about it during the recommendation task. |
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