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Found 13 papers, 4 papers with code
RakutenAI-7B: Extending Large Language Models for Japanese
We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models.
Dynamic Scheduled Sampling with Imitation Loss for Neural Text Generation
Scheduled sampling is a curriculum learning strategy that gradually exposes the model to its own predictions during training to mitigate this bias.
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling
Given the claims of improved text generation quality across various pre-trained neural models, we consider the coherence evaluation of machine generated text to be one of the principal applications of coherence models that needs to be investigated.
DiP Benchmark Tests: Evaluation Benchmarks for Discourse Phenomena in MT
Despite increasing instances of machine translation (MT) systems including extrasentential context information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.
Pronoun-Targeted Fine-tuning for NMT with Hybrid Losses
Our sentence-level model shows a 0. 5 BLEU improvement on both the WMT14 and the IWSLT13 De-En testsets, while our contextual model achieves the best results, improving from 31. 81 to 32 BLEU on WMT14 De-En testset, and from 32. 10 to 33. 13 on the IWSLT13 De-En testset, with corresponding improvements in pronoun translation.
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks
Although coherence modeling has come a long way in developing novel models, their evaluation on downstream applications for which they are purportedly developed has largely been neglected.
Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation
Despite increasing instances of machine translation (MT) systems including contextual information, the evidence for translation quality improvement is sparse, especially for discourse phenomena.
Zero-Resource Cross-Lingual Named Entity Recognition
Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features.
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite
The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations.
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST).
Evaluating Dialogs based on Grice's Maxims
There is no agreed upon standard for the evaluation of conversational dialog systems, which are well-known to be hard to evaluate due to the difficulty in pinning down metrics that will correspond to human judgements and the subjective nature of human judgment itself.
