Papers with CDS
Learning to Understand Child-directed and Adult-directed Speech (2020.acl-main)
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| Challenge: | linguistic properties of child-directed speech differ from adult-directed in many ways . linguistic differences between CDS and ADS are retained, but the acoustic properties are similar. |
| Approach: | They compare the task performance of models trained on adult-directed speech and child-directed language . they propose that CDS is optimized for learnability, but not for comprehension . |
| Outcome: | The proposed model trains on adult-directed speech and child-directed language . the model generalizes better on the training register and on synthesized speech . |
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation (2022.emnlp-main)
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| Challenge: | Existing methods to improve NAT model's performance but do not fully utilize it. |
| Approach: | They propose a non-autoregressive translation method which can obtain high-quality translations while maintaining the inference speed of NAT models. |
| Outcome: | The proposed method outperforms the autoregressive translation model on three translation tasks with 7.6 speedup. |
Corpora of Disordered Speech in the Light of the GDPR: Two Use Cases from the DELAD Initiative (2020.lrec-1)
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| Challenge: | Corpora of disordered speech (CDS) are costly to collect and difficult to share due to personal data protection and IP issues. |
| Approach: | a new paper examines the legal grounds for processing corpora of disordered speech . it illustrates how consent and public interest are taken into consideration . the paper also examines how public interest research can be used to obtain consent . |
| Outcome: | a new study examines the legal grounds for processing corpora of disordered speech (CDS) two use cases illustrate the legal basis for processing CDS in light of the GDPR . |
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but the complexity of emerging tasks and higher performance demands highlight the need for continuous improvement. |
| Approach: | They propose a method that refines evaluation results and characterizes model profiles at the knowledge component level. |
| Outcome: | The proposed method improves performance across multiple benchmarks and academic exams. |
Contrastive Distant Supervision for Debiased and Denoised Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | Distant supervision (DS) is a promising learning approach for machine reading comprehension (MRC) however, the annotated dataset will inevitably lead to mislabeled instances, resulting in answer bias and context noise problems. |
| Approach: | They propose an algorithm that can learn to distinguish confusing and noisy instances via confidence-aware contrastive learning. |
| Outcome: | The proposed algorithm can learn to distinguish confusing and noisy instances via confidence-aware contrastive learning. |
It’s All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution (D19-1)
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| Challenge: | Existing attempts to mitigate gender bias rely on operationalisation of gender bias as a projection over a linear subspace. |
| Approach: | They propose to operationalise gender bias as a linear subspace and augmented a corpus to remove bias by swapping all inherently-gendered words in the copy. |
| Outcome: | The proposed approach outperforms projection-based methods at the task of drawing non-biased gender analogies by an average of 19% across both corpora. |
SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT (2023.acl-long)
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| Challenge: | NLP literature has not given enough attention to the phenomenon of negative transfer . positive transfer refers to the facilitating effects of one language in acquiring another and negative transfer refer to the negative effects between the learner's native [L1] and target [L2] languages. |
| Approach: | They build a Mutlilingual Age Ordered CHILDES dataset to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
| Outcome: | The proposed model enables us to understand the degree to which native Child-Directed Speech (CDS) can help or conflict with English language acquisition. |
Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification (2023.acl-long)
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| Challenge: | Existing semi-supervised text classification methods suffer from categorical boundary issues . existing methods suffer by ambiguous categoric boundaries, making it difficult to generate reliable pseudo-labels for each category. |
| Approach: | They propose a semi-supervised framework that assigns pseudo-labels to unlabeled data . they exploit categorical prototypes to assimilate instance representations within the same category . |
| Outcome: | Empirical studies show that the proposed framework is effective . it uses prototypical cluster separation and prototypical-center data selection . |