Papers with syntactic complexity

9 papers
Controllable Text Simplification with Lexical Constraint Loss (P19-2)

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Challenge: Existing models that only consider the sentence level generate words beyond the target level.
Approach: They propose a method to control the level of a sentence in a text simplification task . they add the target grade level as input and weight words in the loss function .
Outcome: The proposed method improves both BLEU and SARI scores and achieves aggressive rewriting.
MovieCORE: COgnitive REasoning in Movies (2025.emnlp-main)

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Challenge: MovieCORE is a video question answering dataset that focuses on surface-level comprehension.
Approach: They propose a video question-answer dataset that uses large language models as thought agents to generate and refine high-quality question-anchor pairs.
Outcome: The proposed model improves model reasoning capabilities post-training by 25% . the proposed model is based on a large language model and is scalable to a wide range of tasks .
Linguistic Complexity Loss in Text-Based Therapy (2021.naacl-main)

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Challenge: linguistic complexity loss in text-based therapy can be used to identify patterns of mental health . authors: clients who reported more anxiety used less lexically diverse language .
Approach: They analyze linguistic complexity loss in online therapy conversations as it relates to mental health . they find that clients used less lexically diverse language when they were more anxious .
Outcome: The proposed analysis shows that therapists use more complex language when clients are anxious . the authors show that analyzing linguistic complexity can identify meaningful patterns in mental health .
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns (2022.acl-long)

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Challenge: Existing work to predict gaze patterns during naturalistic reading has not been conducted on general text characteristics.
Approach: They propose to use two eye-tracking corpora of naturalistic reading and two language models to test their performance.
Outcome: The proposed models predict eye-tracking measures during naturalistic reading and language processing.
EXPRES Corpus for A Field-specific Automated Exploratory Study of L2 English Expert Scientific Writing (2022.lrec-1)

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Challenge: Developing proficient writing skills in English is a debated topic since the 1990s . RAs are an academic genre that hold a central place in academia .
Approach: They propose to use a linguistic assessment model to assess the linguistic profile of research articles written in L2 English.
Outcome: The proposed model can help scholars adapt to the writing norms of their communities of practice.
Limited Linguistic Diversity in Embodied AI Datasets (2026.acl-long)

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Challenge: Language is a key modality in Vision-Language-Action (VLA) models, yet its linguistic characteristics are poorly documented.
Approach: They propose to audit VLA corpora to characterize what kinds of instructions they contain . they quantify instruction language along complementary dimensions including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity.
Outcome: The proposed dataset audit of several widely used VLA datasets shows that many datasets rely on highly repetitive, template-like commands with limited structural variation yielding a narrow distribution of instruction forms.
Controllable Sentence Simplification (2020.lrec-1)

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Challenge: Text simplification is often considered an all-purpose generic task where the same simplifications are suitable for all but multiple audiences can benefit from simplified text in different ways.
Approach: They propose a controllable simplification model that provides explicit control on simplification systems based on Sequence-to-Sequence models.
Outcome: The proposed model outperforms standard models on simplification benchmarks.
WojoodRelations: Arabic Relation Extraction Corpus and Modeling (2025.emnlp-main)

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Challenge: Existing work on Arabic RE remains limited due to the language’s rich morphology and syntactic complexity, and the lack of large, high-quality datasets.
Approach: They propose to use WojoodRelations to extract relation relationships from Arabic textual data using relation-aware templates and GPT-Joint to perform relation-based retrieval.
Outcome: The proposed method achieves a Cohen’s of 0.92, indicating high reliability, and supervised models achieve 92.89% F1 for RE, while LLMs obtain 72.73% F1 .
AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts (2025.emnlp-main)

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Challenge: Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, especially in high-stake settings such as persuasive online discourse.
Approach: They propose to use general-purpose linguistic features and domain-specific features related to argument quality to compare human- and LLM-authored arguments.
Outcome: The proposed framework compares arguments by humans and three LLMs using two easily-interpretable feature sets.

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