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Textual analogies that make comparisons between two concepts are often used for explaining complex ideas, creative writing, and scientific discovery. In this paper, we propose and study a new task, called Analogy Detection and Extraction (AnaDE), which includes three synergistic sub-tasks: 1) detecting documents containing analogies, 2) extracting text segments that make up the analogy, and 3) identifying the (source and target) concepts being compared. To facilitate the study of this new task, we create a benchmark dataset by scraping Metamia.com and investigate the performances of state-of-the-art models on all sub-tasks to establish the first-generation benchmark results for this new task. We find that the Longformer model achieves the best performance on all the three sub-tasks demonstrating its effectiveness for handling long texts. Moreover, smaller models fine-tuned on our dataset perform better than non-finetuned ChatGPT, suggesting high task difficulty. Overall, the models achieve a high performance on documents detection suggesting that it could be used to develop applications like analogy search engines. Further, there is a large room for improvement on the segment and concept extraction tasks.more » « lessFree, publicly-accessible full text available March 22, 2025
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Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional AnalysisComputing self-efficacy is an important factor in shaping students' motivation, performance, and persistence in computer science (CS) courses. Therefore, investigating computing self-efficacy may help to improve the persistence of students from historically underrepresented groups in computing. Previous research has shown that computing self-efficacy is positively correlated with prior computing experience, but negatively correlated with some demographic identities (e.g., identifying as a woman). However, existing research has not demonstrated these patterns on a large scale while controlling for confounding variables and institutional context. In addition, there is a need to study the experiences of students with multiple marginalized identities through the lens of intersectionality. Our goal is to investigate the relationship between students' computing self-efficacy and their prior experience in computing, demographic identities, and institutional policies. We conduct this investigation using a large, recent, and multi-institutional dataset with survey responses from 31,425 students. Our findings confirm that more computing experience positively predicts computing self-efficacy. However, identifying as Asian, Black, Native, Hispanic, non-binary, and/or a woman were statistically significantly associated with lower computing self-efficacy. The results of our work point to several future avenues for self-efficacy research in computing.more » « less
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Successful women role models can be—but are not always—effective in increasing pursuit of science, technology, engineering, and mathematics (STEM) careers among girls. What makes a woman role model motivating for young girls? An experimental study (N = 205 girls aged 5–8 years; 42.0% girls of color) investigated the effects of a role model’s messages about her own ability and interest. The model portrayed her ability and interest as quantities that developed over time (a growth mindset) or that had always been present (a fixed mindset). The role model’s growth (vs. fixed) mindset messages about ability—but not interest—increased girls’ interest and self-efficacy in the scientist’s field, but these effects were observed only among girls of color (ds = 0.56 and 0.65 for interest and self-efficacy, respectively). The findings contribute to theory on role models and growth mindsets, and they also have implications for the design of effective role model interventions.more » « less
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Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings.more » « less
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Idiomatic expression (IE) processing and comprehension have challenged pre-trained language models (PTLMs) because their meanings are non-compositional. Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs. This extends the established ATOMIC2020 graph, converting PTLMs into knowledge models (KMs) that encode and infer commonsense knowledge related to IE use. Experiments show that various PTLMs can be converted into KMs with IEKG. We verify the quality of IEKG and the ability of the trained KMs with automatic and human evaluation. Through applications in natural language understanding, we show that a PTLM injected with knowledge from IEKG exhibits improved IE comprehension ability and can generalize to IEs unseen during training.more » « less
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Accurate processing of non-compositional language relies on generating good representations for such expressions. In this work, we study the representation of language non-compositionality by proposing a language model, PIER+, that builds on BART and can create semantically meaningful and contextually appropriate representations for English potentially idiomatic expressions (PIEs). PIEs are characterized by their non-compositionality and contextual ambiguity in their literal and idiomatic interpretations. Via intrinsic evaluation on embedding quality and extrinsic evaluation on PIE processing and NLU tasks, we show that representations generated by PIER+ result in 33% higher homogeneity score for embedding clustering than BART, whereas 3.12% and 3.29% gains in accuracy and sequence accuracy for PIE sense classification and span detection compared to the state-of-the-art IE representation model, GIEA. These gains are achieved without sacrificing PIER+’s performance on NLU tasks (+/- 1% accuracy) compared to BART.more » « less
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Non-compositional expressions, by virtue of their non-compositionality, are a classic ‘pain in the neck’ for NLP systems. Different from the general language modeling and generation tasks that are primarily compositional, generating non-compositional expressions is more challenging for current neural models, including large pre-trained language models. The main reasons are 1) their non-compositionality, and 2) the limited data resources. Therefore, to make the best use of available data for modeling non-compositionality, we propose a dynamic curriculum learning framework, which learns training examples from easy ones to harder ones thus optimizing the learning step by step but suffers from the forgetting problem. To alleviate the forgetting problem brought by the arrangement of training examples, we also apply a continual learning method into our curriculum learning framework. Our proposed method combined curriculum and continual learning, to gradually improve the model’s performance on the task of non-compositional expression generation. Experiments on idiomatic expression generation and metaphor generation affirm the effectiveness of our proposed curriculum learning framework and the application of continual learning. Our codes are available at https://github.com/zhjjn/CL2Gen.git.more » « less