Search Results for author:
Found 8 papers, 2 papers with code
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
We conduct a large-scale fine-grained comparative analysis of machine translations (MT) against human translations (HT) through the lens of morphosyntactic divergence.
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
We present a large empirical study quantifying the sometimes severe loss in performance (up to 12 ROUGE-1 points) from different types of input noise for a range of datasets and model sizes.
Prompting PaLM for Translation: Assessing Strategies and Performance
Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages.
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Furthermore, the space of potential low-quality outputs is larger as arbitrary text can be generated and it is important to know when to trust the generated output.
Abstractive Text Summarization
Out-of-Distribution Detection
+1
Deciphering Undersegmented Ancient Scripts Using Phonetic Prior
We evaluate the model on both deciphered languages (Gothic, Ugaritic) and an undeciphered one (Iberian).
Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
In this paper we propose a novel neural approach for automatic decipherment of lost languages.
Towards Decomposed Linguistic Representation with Holographic Reduced Representation
The vast majority of neural models in Natural Language Processing adopt a form of structureless distributed representations.
Unsupervised Learning of Morphological Forests
This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary.
