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Found 13 papers, 4 papers with code
Finding Replicable Human Evaluations via Stable Ranking Probability
Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult.
The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
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FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware Machine Translation, a type of style-targeted translation.
Outside Computation with Superior Functions
We show that a general algorithm for efficient computation of outside values under the minimum of superior functions framework proposed by Knuth (1977) would yield a sub-exponential time algorithm for SAT, violating the Strong Exponential Time Hypothesis (SETH).
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
Unlike previous approaches requiring style-labeled training data, our method makes use of readily-available unlabeled text by relying on the implicit connection in style between adjacent sentences, and uses labeled data only at inference time.
TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling
We present a novel approach to the challenging problem of label-free text style transfer.
Translationese as a Language in ``Multilingual'' NMT
Machine translation has an undesirable propensity to produce {``}translationese{''} artifacts, which can lead to higher BLEU scores while being liked less by human raters.
Unsupervised Bilingual Lexicon Induction Across Writing Systems
Recent embedding-based methods in unsupervised bilingual lexicon induction have shown good results, but generally have not leveraged orthographic (spelling) information, which can be helpful for pairs of related languages.
Translationese as a Language in "Multilingual" NMT
Machine translation has an undesirable propensity to produce "translationese" artifacts, which can lead to higher BLEU scores while being liked less by human raters.
Feature-Based Decipherment for Machine Translation
The existing decipherment models, however, are not well suited for exploiting these orthographic similarities.
Orthographic Features for Bilingual Lexicon Induction
Recent embedding-based methods in bilingual lexicon induction show good results, but do not take advantage of orthographic features, such as edit distance, which can be helpful for pairs of related languages.
