132 research outputs found
On Efficient Training, Controllability and Compositional Generalization of Insertion-based Language Generators
Auto-regressive language models with the left-to-right generation order have
been a predominant paradigm for language generation. Recently, out-of-order
text generation beyond the traditional left-to-right paradigm has attracted
extensive attention, with a notable variation of insertion-based generation,
where a model is used to gradually extend the context into a complete sentence
purely with insertion operations. However, since insertion operations disturb
the position information of each token, it is often believed that each step of
the insertion-based likelihood estimation requires a bi-directional
\textit{re-encoding} of the whole generated sequence. This computational
overhead prohibits the model from scaling up to generate long, diverse texts
such as stories, news articles, and reports. To address this issue, we propose
InsNet, an insertion-based sequence model that can be trained as efficiently as
traditional transformer decoders while maintaining the same performance as that
with a bi-directional context encoder. We evaluate InsNet on story generation
and CleVR-CoGENT captioning, showing the advantages of InsNet in several
dimensions, including computational costs, generation quality, the ability to
perfectly incorporate lexical controls, and better compositional
generalization
Long Text Generation via Adversarial Training with Leaked Information
Automatically generating coherent and semantically meaningful text has many
applications in machine translation, dialogue systems, image captioning, etc.
Recently, by combining with policy gradient, Generative Adversarial Nets (GAN)
that use a discriminative model to guide the training of the generative model
as a reinforcement learning policy has shown promising results in text
generation. However, the scalar guiding signal is only available after the
entire text has been generated and lacks intermediate information about text
structure during the generative process. As such, it limits its success when
the length of the generated text samples is long (more than 20 words). In this
paper, we propose a new framework, called LeakGAN, to address the problem for
long text generation. We allow the discriminative net to leak its own
high-level extracted features to the generative net to further help the
guidance. The generator incorporates such informative signals into all
generation steps through an additional Manager module, which takes the
extracted features of current generated words and outputs a latent vector to
guide the Worker module for next-word generation. Our extensive experiments on
synthetic data and various real-world tasks with Turing test demonstrate that
LeakGAN is highly effective in long text generation and also improves the
performance in short text generation scenarios. More importantly, without any
supervision, LeakGAN would be able to implicitly learn sentence structures only
through the interaction between Manager and Worker.Comment: 14 pages, AAAI 201
Texygen: A Benchmarking Platform for Text Generation Models
We introduce Texygen, a benchmarking platform to support research on
open-domain text generation models. Texygen has not only implemented a majority
of text generation models, but also covered a set of metrics that evaluate the
diversity, the quality and the consistency of the generated texts. The Texygen
platform could help standardize the research on text generation and facilitate
the sharing of fine-tuned open-source implementations among researchers for
their work. As a consequence, this would help in improving the reproductivity
and reliability of future research work in text generation.Comment: 4 page
Acyl-CoA Dehydrogenase Drives Heat Adaptation by Sequestering Fatty Acids
Cells adapt to temperature shifts by adjusting levels of lipid desaturation and membrane fluidity. This fundamental process occurs in nearly all forms of life, but its mechanism in eukaryotes is unknown. We discovered that the evolutionarily conserved C. elegans gene acdh-11 (acyl CoAdehydrogenase, ACDH) facilitates heat adaptation by regulating the lipid desaturase FAT-7. Human ACDH deficiency causes the most common inherited disorders of fatty acid oxidation, with syndromes that are exacerbated by hyperthermia. Heat up-regulates acdh-11 expression to
decrease fat-7 expression. We solved the high-resolution crystal structure of ACDH-11 and established the molecular basis of its selective and high-affinity binding to C11/C12-chain fatty acids. ACDH-11 sequesters C11/C12-chain fatty acids and prevents these fatty acids from activating nuclear hormone receptors and driving fat-7 expression. Thus, the ACDH-11 pathway drives heat adaptation by linking temperature shifts to regulation of lipid desaturase levels and membrane fluidity via an unprecedented mode of fatty acid signaling.National Institutes of Health (U.S.) (Grants GM24663 and K99HL11665)Charles A. King Trust (Postdoctoral Fellowship
MEPE/OF45 protects cells from DNA damage induced killing via stabilizing CHK1
Matrix extracellular phosphoglycoprotein/osteoblast factor 45 (MEPE/OF45) was cloned in 2000 with functions related to bone metabolism. We identified MEPE/OF45 for the first time as a new co-factor of CHK1 in mammalian cells to protect cells from DNA damage induced killing. We demonstrate here that MEPE/OF45 directly interacts with CHK1. Knocking down MEPE/OF45 decreases CHK1 levels and sensitizes the cells to DNA damage inducers such as ionizing radiation (IR) or camptothicin (CPT)-induced killing. Over-expressing wild-type MEPE/OF45, but not the mutant MEPE/OF45 (depleted the key domain to interact with CHK1) increases CHK1 levels in the cells and increases the resistance of the cells to IR or CPT. MEPE/OF45, interacting with CHK1, increases CHK1 half-life and decreases CHK1 degradation through the ubiquitine-mediated pathway. In addition, the interaction of MEPE/OF45 with CHK1 decreases CHK1 levels in the ubiquitin E3 ligases (Cul1 and Cul4A) complex, which suggests that MEPE/OF45 competes with the ubiquitin E3 ligases binding to CHK1 and thus decreases CHK1 from ubiquitin-mediated proteolysis. These findings reveal an important role of MEPE/OF45 in protecting cells from DNA damage induced killing through stabilizing CHK1, which would provide MEPE/OF45 as a new target for sensitizing tumor cells to radiotherapy or chemotherapy
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