United States

Stories are central for interpreting experiences, communicating, and influencing each other via films, medical, media, and other narra tives. Quantifying the similarity between sto ries has numerous applications including detect ing IP infringement, detecting hallucinations, search/recommendation engines, and guiding human-AI collaborations. Despite this, tradi tional NLP text similarity metrics are limited to short text distance metrics like n-gram over laps and embeddings. Larger texts require pre processing with significant information loss through paraphrasing or multi-step decomposi tion. This paper introduces AIStorySimilarity, a novel benchmark to measure the semantic dis tance between long-text stories based on core structural elements drawn from narrative the ory and script writing. Based on four narrative elements (characters, plot, setting, and themes) as well 31 sub-features within these, we use a SOTA LLM (gpt-3.5-turbo) to extract and evaluate the semantic similarity of a diverse set of major Hollywood movies. In addition, we compare human evaluation with story simi larity scores computed three ways: extracting elements from film scripts before evaluation (Elements), directly evaluating entire scripts (Scripts), and extracting narrative elements from the parametric memory of SOTA LLMs without any provided scripts (GenAI). To the best of our knowledge, AIStorySimilarity is the first benchmark to measure long-text story similarity using a comprehensive approach to narrative theory. All code, data, and plot image f iles are available at https://github.com/ jon-chun/AIStorySimiliarity.

EMNLP 2024

AIStorySimilarity: Quantifying Story Similarity Using Narrative for Search, IP Infringement, and Guided Creativity

story recommendation

ai creativity

ai hallucination

ai intellectual property

generative ai

text similarity

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Sentiment analysis is crucial in Natural Lan guage Processing as it enables the extraction of opinions and emotions from text. How ever, Arabic sentiment analysis is often over looked. Current benchmarks for Arabic senti ment analysis tend to be outdated or lack com prehensive annotations, which limits the devel opment of more accurate and reliable models for the Arabic language. To address these chal lenges, we introduce ArSen, a meticulously an notated Arabic dataset centered on COVID-19, along with IFDHN, a novel model that employs fuzzy logic for more precise sentiment classifi cation1. ArSen offers a robust and contempo rary benchmark, and IFDHN achieves state-of the-art performance in Arabic sentiment analy sis, with 78.12% accuracy, an F1-Macro score of 55.83%, and an F1-Micro score of 78.12% on the test set. Notably, by using only 0.23% of the computational resources of large language models, IFDHN achieved performance compa rable to LLaMA-3-8B, showcasing significant improvements over existing methods.

Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network - Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, Yuke Mei

The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be miss- ing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterar- guments, we use them to question arguments, without requiring any external knowledge. Re- search on CQs Generation using LLMs requires a reference dataset for large scale experimenta- tion. Thus, in this work we investigate two com- plementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton’s argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task.

Critical Questions Generation: Motivation and Challenges

Humans develop their grammars by making structural generalizations from finite input. We ask how filler-gap dependencies (FGDs), which share a structural generalization despite diverse surface forms, might arise from the input. We explicitly control the input to a neural language model (NLM) to uncover whether the model posits a shared representation for FGDs. We show that while NLMs do have success differentiating grammatical from ungrammatical FGDs, they rely on superficial properties of the input, rather than on a shared generalization. Our work highlights the need for specific linguistic inductive biases to model language acquisition.

Generalizations across filler-gap dependencies in neural language models

The data-driven investigation of the extent to which lexicons of different languages align has mostly fallen into one of two categories: colexification-based and distributional. The two approaches are grounded in distinct methodologies, operate on different assumptions, and are used in diverse ways. This raises two important questions: (a) are there settings in which the predictions of the two approaches can be directly compared? and if so, (b) what is the extent of the similarity and what are its determinants? We offer novel operationalizations for the two approaches in a manner that allows for their direct comparison, and conduct a comprehensive analysis on a diverse set of 16 languages. Our analysis is carried out at different levels of granularity. At the word-level, the two methods present different results across the board. However, intriguingly, at the level of semantic domains (e.g., kinship, quantity), the two methods show considerable convergence in their predictions. Our findings also indicate that the distributional methods likely capture a more fine-grained alignment than their counterpart colexification-based methods, and may thus be more suited for settings where fewer languages are evaluated.

Aligning Alignments: Do Colexification and Distributional Similarity Align as Measures of cross-lingual Lexical Alignment?

We study some Large Language Models to explore their deficiencies in resolving sense ambiguities. In this connection, we evaluate their performance on well-known word sense disambiguation datasets. Word Sense Disambiguation (WSD) has been a long-standing NLP problem, which has given rise to many evaluation datasets and models over the decades. Recently the emergence of Large Language Models (LLM) raises much hope in improving accuracy. In this work, we evaluate word sense disambiguation capabilities of four LLMs: OpenAI’s ChatGPT-3.5, Mistral’s 7b parameter model, Meta’s Llama 70b, and Google’s Gemini Pro. We evaluate many well-established datasets containing a variety of texts and senses on these. After observing the performances of some datasets, we selectively study some failure cases and identify the reasons for failures. We explore human judgments that would correct these failures. Our findings suggest that many failure cases are related to a lack of world knowledge and the reasoning to amalgamate this knowledge rather than the lack of linguistic knowledge. We categorize the judgments so that the next generation of LLMs can improve by incorporating deeper world knowledge and reasoning. We conclude that word sense disambiguation could serve as a guide for probing the reasoning power of LLMs to measure their functional competency. We also list the accuracy of these datasets. We find that on many occasions, accuracy drops to below 70%, which is much less than that of well-performing existing models.

On Functional Competence of LLMs for Linguistic Disambiguation

Extracting adverse reactions to medications or treatments is a crucial activity in the biomedical domain. The task involves identifying mentions of drugs and their adverse effects/events in raw text, which is challenging due to the unstructured nature of clinical narratives. In this paper, we propose TpT-ADE, a novel joint twophase transformer model combined with natural language processing (NLP) techniques, to identify adverse events (AEs) caused by drugs. In the first phase of TpT-ADE, entities are extracted and are grounded with their standard terms using the Unified Medical Language System (UMLS) knowledge base. In the second phase, entity and relation classification is performed to determine the presence of a relationship between the drug and AE pairs. TpT-ADE also identifies the intensity of AE entities by constructing a parts-of-speech (POS) embedding model. Unlike previous approaches that use complex classifiers, TpT-ADE employs a shallow neural network and yet outperforms the state-of-the-art methods on the standard ADE corpus.

TpT-ADE: Transformer Based Two-Phase ADE Extraction

Recent advances in computational linguistics include simulating the emergence of human-like languages with interacting neural network agents, starting from sets of random symbols. The recently introduced NeLLCom framework (Lian et al., 2023) allows agents to first learn an artificial language and then use it to communicate, with the aim of studying the emergence of specific linguistics properties. We extend this framework (NeLLCom-X) by introducing more realistic role-alternating agents and group communication in order to investigate the interplay between language learnability, communication pressures, and group size effects. We validate NeLLCom-X by replicating key findings from prior research simulating the emergence of a word-order/case-marking trade-off. Next, we investigate how interaction affects linguistic convergence and emergence of the trade-off. The novel framework facilitates future simulations of diverse linguistic aspects, emphasizing the importance of interaction and group dynamics in language evolution.

NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication

We introduce the Principled Reasoning and Acting (PRAct) framework, a novel LLM agent for learning and enforcing action principles from trajectory data.

PRACT: Optimizing Principled Reasoning and Acting of LLM Agent

We study attribute control in language models through the method of Causal Average Treatment Effect (Causal ATE). Existing methods for the attribute control task in Language Models (LMs) check for the co-occurrence of words in a sentence with the attribute of interest, and control for them. However, spurious correlation of the words with the attribute in the training dataset, can cause models to hallucinate the presence of the attribute when presented with the spurious correlate during inference. We show that the simple perturbation-based method of Causal ATE removes this unintended effect. Specifically, we ground it in the problem of toxicity mitigation, where a significant challenge lies in the inadvertent bias that often emerges towards protected groups post detoxification. We show that this unintended bias can be solved by the use of the Causal ATE metric. We provide experimental validations for our claims and release our code (anonymously) here: \href{https://github.com/causalate-mitigates-bias/causal-ate-mitigates-bias}{github.com/causalate-mitigates-bias/causal-ate-mitigates-bias}.

Causal ATE Mitigates Unintended Bias in Controlled Text Generation

Text Segmentation (TS) is the task of segmenting bodies of text into coherent blocks, mostly defined by the topics each segment contains. Historically, techniques in this area have been unsupervised, with more success recently coming from supervised methods instead. Although these approaches see better performance, they require training data and upfront training time. We propose a new method called Coherence, where we use sentence embeddings to pull representational keywords as the main constructor of sentences when comparing them to one another. Additionally, we include a storage of previously found keywords for the purposes of creating a more accurate segment representation instead of just the immediate sentence in question. We show improved results over current state-of-the-art unsupervised techniques when analyzed using Pk and WindowDiff scores. Coherence also requires no fine-tuning.

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Next from EMNLP 2024

Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network - Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, Yuke Mei

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Next from EMNLP 2024

Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network - Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, Yuke Mei

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