Faeze Brahman


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Proceedings of the Third Workshop on Narrative Understanding
Nader Akoury | Faeze Brahman | Snigdha Chaturvedi | Elizabeth Clark | Mohit Iyyer | Lara J. Martin
Proceedings of the Third Workshop on Narrative Understanding

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ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi | Arman Cohan | Siamak Shakeri | Pedram Hosseini | Pouya Pezeshkpour | Malihe Alikhani | Moin Aminnaseri | Marzieh Bitaab | Faeze Brahman | Sarik Ghazarian | Mozhdeh Gheini | Arman Kabiri | Rabeeh Karimi Mahabagdi | Omid Memarrast | Ahmadreza Mosallanezhad | Erfan Noury | Shahab Raji | Mohammad Sadegh Rasooli | Sepideh Sadeghi | Erfan Sadeqi Azer | Niloofar Safi Samghabadi | Mahsa Shafaei | Saber Sheybani | Ali Tazarv | Yadollah Yaghoobzadeh
Transactions of the Association for Computational Linguistics, Volume 9

Abstract Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1

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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
Faeze Brahman | Meng Huang | Oyvind Tafjord | Chao Zhao | Mrinmaya Sachan | Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: EMNLP 2021

When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.

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Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Tenghao Huang | Faeze Brahman | Vered Shwartz | Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: EMNLP 2021

Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (“women can’t park”) or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist’s motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters’ portrayal is centered around appearance, while male figures’ focus on intellect.

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Is Everything in Order? A Simple Way to Order Sentences
Somnath Basu Roy Chowdhury | Faeze Brahman | Snigdha Chaturvedi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine’s understanding of causal and temporal relations. We formulate the sentence ordering task as a conditional text-to-marker generation problem. We present Reorder-BART (Re-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text. Re-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall’s tau. We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform several experiments to understand the functioning and limitations of our framework.


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Cue Me In: Content-Inducing Approaches to Interactive Story Generation
Faeze Brahman | Alexandru Petrusca | Snigdha Chaturvedi
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Automatically generating stories is a challenging problem that requires producing causally related and logical sequences of events about a topic. Previous approaches in this domain have focused largely on one-shot generation, where a language model outputs a complete story based on limited initial input from a user. Here, we instead focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions in the form of cue phrases during the generation process. This provides an interface for human users to guide the story generation. We present two content-inducing approaches to effectively incorporate this additional information. Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories compared to baseline methods.

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Modeling Protagonist Emotions for Emotion-Aware Storytelling
Faeze Brahman | Snigdha Chaturvedi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.