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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.
arXiv (opens in a new tab)

Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning

We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety…
ACL (opens in a new tab)

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks.
arXiv (opens in a new tab)

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

The results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.
IEEE (opens in a new tab)

Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation…
ACL (opens in a new tab)

PaLI: A Jointly-Scaled Multilingual Language-Image Model

The PaLI (Pathways Language and Image model), a model that achieves state-of-the-art in multiple vision and language tasks, while retaining a simple, modular, and scalable design.
arXiv (opens in a new tab)

Findings of the 2012 Workshop on Statistical Machine Translation

A large-scale manual evaluation of 103 machine translation systems submitted by 34 teams was conducted, which used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 12 evaluation metrics.
ACL (opens in a new tab)

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

This work proposes a simple, general recipe to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
arXiv (opens in a new tab)

Sentence Level Discourse Parsing using Syntactic and Lexical Information

Two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees are introduced and shown to be sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance.
ACL (opens in a new tab)

Findings of the 2013 Workshop on Statistical Machine Translation

We present the results of the WMT13 shared tasks, which included a translation task, a task for run-time estimation of machine translation quality, and an unofficial metrics task. This year, 143…
ACL (opens in a new tab)
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