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Found 16 papers, 9 papers with code
Language Is Not All You Need: Aligning Perception with Language Models
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.
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Image Captioning
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TorchScale: Transformers at Scale
Large Transformers have achieved state-of-the-art performance across many tasks.
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning
In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more parameter efficient, thereby promoting better adoption in resource-constrained scenarios and practical applications.
Language Model Decoding as Likelihood-Utility Alignment
Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm.
Foundation Transformers
A big convergence of model architectures across language, vision, speech, and multimodal is emerging.
On the Representation Collapse of Sparse Mixture of Experts
We also present a comprehensive analysis on the representation and routing behaviors of our models.
On Efficiently Acquiring Annotations for Multilingual Models
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages.
Invariant Language Modeling
In particular, we adapt a game-theoretic formulation of IRM (IRM-games) to language models, where the invariance emerges from a specific training schedule in which all the environments compete to optimize their own environment-specific loss by updating subsets of the model in a round-robin fashion.
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
State of the art research for date-time entity extraction from text is task agnostic.
ScopeIt: Scoping Task Relevant Sentences in Documents
Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based conversational agents, the communication is asynchronous, and often contains information irrelevant to the assistant.
Weakly Supervised Attention Networks for Entity Recognition
The task of entity recognition has traditionally been modelled as a sequence labelling task.
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
Compression and Localization in Reinforcement Learning for ATARI Games
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed.
BLISS in Non-Isometric Embedding Spaces
We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) --- a novel semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique.
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain.
