Corpus Query Language (CQL) supports precise corpus analysis but requires expertise in annotations, regular expressions, structural scopes, and platform-specific syntax. Natural-language interfaces simplify access, but their answers can be difficult to inspect, reproduce, or revise. We study Text-to-CQL as executable semantic parsing, mapping intents to inspectable queries. We introduce ECQL, a Sketch-Engine-oriented benchmark of 3,572 natural language–CQL pairs comprising 811 tutorial-derived and 2,761 synthetic examples, with a default split of 3,215 training and 357 test examples. ECQL focuses on Sketch Engine normalization, CQL Diagnostic Profile (CQL-DP) annotations, controlled semantic contrasts, audit-oriented metadata, and generalization protocols, alongside an evaluation suite covering syntactic validity, exact match, canonical match, and execution match. The full 357-example headline test set is independently double-annotated and adjudicated, with 96.9% agreement for intent clarity, 94.7% agreement for CQL–intent alignment, and a 3.1% post-adjudication intent-match error rate. Experiments with prompting, retrieval-augmented prompting, syntax repair, DeepSeek-R1 reasoning prompting, a GPT-4o Agent/API alternative, and supervised fine-tuning reveal persistent difficulties with corpus-query semantics. The best fine-tuned model reaches 85.6% execution accuracy on the random split, but drops to 68.9% on template-disjoint evaluation. ECQL provides a reproducible testbed for auditable semantic parsing over specialized linguistic query languages.

This paper describes our submission for task 5 Multimedia Automatic Misogyny Identification (MAMI) at SemEval-2022. The task is designed to detect and classify misogynous memes. To utilize both textual and visual information presented in a meme, we investigate several of the most recent visual language transformer-based multimodal models and choose ERNIE-ViL-Large as our base model. For subtask A, with observations of models’ overfitting on unimodal patterns, strategies are proposed to mitigate problems of biased words and template memes. For subtask B, we transform this multi-label problem into a multi-class one and experiment with oversampling and complementary techniques. Our approach places 2nd for subtask A and 5th for subtask B in this competition.

The memes serve as an important tool in online communication, whereas some hateful memes endanger cyberspace by attacking certain people or subjects. Recent studies address hateful memes detection while further understanding of relationships of entities in memes remains unexplored. This paper presents our work at the Constraint@ACL2022 Shared Task: Hero, Villain and Victim: Dissecting harmful memes for semantic role labelling of entities. In particular, we propose our approach utilizing transformer-based multimodal models through a VCR method with data augmentation, continual pretraining, loss re-weighting, and ensemble learning. We describe the models used, the ways of preprocessing and experiments implementation. As a result, our best model achieves the Macro F1-score of 54.707 on the test set of this shared task.