| Challenge: | Algorithmic sequence alignment is a common operation in many NLP tasks, but it is difficult to recognize similarities between distant versions of narratives such as translations and retellings. |
| Approach: | They propose a general approach to narrative alignment coupling the Smith-Waterman algorithm from bioinformatics with modern text similarity metrics. |
| Outcome: | The proposed approach can be used to identify similarities between two different versions of narratives, and to define rigorous p-values on the significance of any alignment. |
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AligNarr: Aligning Narratives on Movies (2021.acl-short)
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| Challenge: | Experimental results show the viability of an unsupervised approach to align movie scripts with plot summaries. |
| Approach: | They propose an unsupervised method to align movie scripts with plot summaries using a global optimization model. |
| Outcome: | The proposed method outperforms a baseline alignment model on ten movies with 76% F1 score. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
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| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Analyzing Film Adaptation through Narrative Alignment (2023.emnlp-main)
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| Challenge: | a new study examines the book-to-film adaptation process by examining the differences between the two media . novel adaptations often require dropping sections of the source text from the movie script . |
| Approach: | They use a Smith-Waterman local alignment algorithm to quantify text similarity between scenes and book units. |
| Outcome: | The proposed method reveals that novel adaptations often require dropping parts of the source text from the movie script. |
My side, your side and the evidence: Discovering aligned actor groups and the narratives they weave (2023.acl-long)
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| Challenge: | Identify distinct sets of aligned story actors responsible for sustaining issue-specific narratives . authors propose a novel two-step graph-based framework that identifies alignments between actors . |
| Approach: | They propose a proxy task to identify the distinct sets of aligned story actors . they propose identifying alignments between actors and extracting alignes using TAMPA . |
| Outcome: | The proposed framework is based on a corpus of text segments associated with six issues . it identifies aligned actors and extracts alignable actor groups from the network structure . |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
Story Embeddings — Narrative-Focused Representations of Fictional Stories (2024.emnlp-main)
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| Challenge: | Existing approaches to model fictional narratives have focused on the aspect of "what" rather than "how" they are being told. |
| Approach: | They propose a model that embeds stories such that similar stories will result in similar embeddings. |
| Outcome: | The proposed model shows state-of-the-art performance on multiple retrieval tasks and a narrative understanding task. |
text2story: A Python Toolkit to Extract and Visualize Story Components of Narrative Text (2024.lrec-main)
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| Challenge: | Story components, namely events, time, participants, and their relations, are present in narrative texts from different domains such as journalism, medicine, finance, and law. |
| Approach: | They propose to use an array of narrative extraction tools to extract narratives from text . the package contains an array and an experimental module for evaluation . |
| Outcome: | The text2story python supports the narrative extraction and visualization pipeline. |
Narrative Theory for Computational Narrative Understanding (2021.emnlp-main)
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| Challenge: | a growing body of theoretical work on narrative has been focused on the field of natural language processing . this position paper aims to provide a unifying framework for the computational study of narrative . |
| Approach: | They propose to introduce dominant theoretical frameworks to the NLP community and situate current research within distinct narratological traditions. |
| Outcome: | The proposed framework would allow for new empirical questions and applications in the field of natural language processing. |
Massively Multilingual Document Alignment with Cross-lingual Sentence-Mover’s Distance (2020.aacl-main)
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| Challenge: | Document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. |
| Approach: | They propose an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. |
| Outcome: | The proposed scoring function outperforms baseline methods on high-resource language pairs, 15% on mid-resourced language pairs and 22% on low-resourcing language pairs. |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |