The Flow has not summarised this story yet — read it at arXiv Computation and Language.
E-CONAN introduces Arabic textual entailment and natural inference benchmarks comprising two datasets: E-CONAN-2 (2-way RTE) and E-CONAN-3 (3-way NLI). The datasets are built from automatically-translated pairs, human-validated machine translations, hand-crafted pairs from Arabic teaching books, and rumor-containing news headlines. The authors evaluated nine multilingual pretrained models and five large language models on these benchmarks, demonstrating that E-CONAN offers a more diverse and robust assessment than existing datasets like XNLI and ArNLI.
arXiv:2607.25202v2 Announce Type: replace Abstract: Conversational entrainment is well-studied in monolingual and written contexts, but remains underexplored in spoken code-switching (CSW). We presen...
The study examines whether domain‑adaptive continued pretraining (DAPT) on a learner‑writing corpus (EFCAMDAT) can enhance transformer‑based automated essay scoring (AES) for English proficiency tests. Researchers applied DAPT to BERT, RoBERTa, and DistilBERT and compared the adapted models with their original checkpoints on the FCE and IELTS datasets, evaluating both in‑domain scoring and few‑shot cross‑dataset transfer. Results show that full‑corpus DAPT yields mixed effects, while proficiency‑specific DAPT often outperforms full‑corpus DAPT and sometimes even the non‑adapted baseline, though benefits vary by proficiency composition and encoder architecture and do not consistently transfer across tests.
The paper investigates whether training small decoder-only transformers on code‑switched text can induce cross‑lingual alignment. Using two 100‑million‑word multilingual corpora—one a mix of English, Dutch, and Chinese BabyBabelLM data, and another generated by inserting word‑ and sentence‑level code‑switching via an LLM—the authors find that code‑switched training aligns representations of parallel text, especially across different scripts, and that this alignment persists when later training on monolingual documents. A curriculum that progresses from word‑level code‑switching to sentence‑level code‑switching and finally to monolingual data yields models that outperform baselines on the BabyLM evaluation suite, demonstrating that code‑switching curriculum learning is an effective data augmentation strategy for multilingual pretraining.
The study examines how the geometric placement of synthetic data affects discourse‑pragmatic function classification. Using 410 annotated instances of the word "look" from the British National Corpus, synthetic examples were generated with Llama 3.1 and grouped by cosine distance from real data in RoBERTa space. Six training conditions were compared, showing that examples close to real data (NEAR) yield the largest macro‑F gain, while a distance‑balanced mix gives the highest accuracy, yet none improve AUC.
The study examines how the geometric placement of synthetic data affects discourse-pragmatic function classification. Using 410 annotated instances of the word "look" and synthetic examples generated by Llama 3.1, the authors partitioned the synthetic data by cosine distance in RoBERTa space and compared six training conditions. While all augmentation conditions improved macro‑F and accuracy over a real‑only baseline, the nearest synthetic examples (NEAR) yielded the largest macro‑F gain (0.113) and a distance‑balanced mix achieved the highest accuracy (0.748), though none improved AUC.