25 research outputs found
Guess who? Multilingual approach for the automated generation of author-stylized poetry
This paper addresses the problem of stylized text generation in a
multilingual setup. A version of a language model based on a long short-term
memory (LSTM) artificial neural network with extended phonetic and semantic
embeddings is used for stylized poetry generation. The quality of the resulting
poems generated by the network is estimated through bilingual evaluation
understudy (BLEU), a survey and a new cross-entropy based metric that is
suggested for the problems of such type. The experiments show that the proposed
model consistently outperforms random sample and vanilla-LSTM baselines, humans
also tend to associate machine generated texts with the target author
Portfolio optimization in the case of an asset with a given liquidation time distribution
Management of the portfolios containing low liquidity assets is a tedious
problem. The buyer proposes the price that can differ greatly from the paper
value estimated by the seller, the seller, on the other hand, can not liquidate
his portfolio instantly and waits for a more favorable offer. To minimize
losses in this case we need to develop new methods. One of the steps moving the
theory towards practical needs is to take into account the time lag of the
liquidation of an illiquid asset. This task became especially significant for
the practitioners in the time of the global financial crises. Working in the
Merton's optimal consumption framework with continuous time we consider an
optimization problem for a portfolio with an illiquid, a risky and a risk-free
asset. While a standard Black-Scholes market describes the liquid part of the
investment the illiquid asset is sold at a random moment with prescribed
liquidation time distribution. In the moment of liquidation it generates
additional liquid wealth dependent on illiquid assets paper value. The investor
has the logarithmic utility function as a limit case of a HARA-type utility.
Different distributions of the liquidation time of the illiquid asset are under
consideration - a classical exponential distribution and Weibull distribution
that is more practically relevant. Under certain conditions we show the
existence of the viscosity solution in both cases. Applying numerical methods
we compare classical Merton's strategies and the optimal consumption-allocation
strategies for portfolios with different liquidation-time distributions of an
illiquid asset.Comment: 30 pages, 1 figur
What is Wrong with Language Models that Can Not Tell a Story?
This paper argues that a deeper understanding of narrative and the successful
generation of longer subjectively interesting texts is a vital bottleneck that
hinders the progress in modern Natural Language Processing (NLP) and may even
be in the whole field of Artificial Intelligence. We demonstrate that there are
no adequate datasets, evaluation methods, and even operational concepts that
could be used to start working on narrative processing
Vocabulary Transfer for Medical Texts
Vocabulary transfer is a transfer learning subtask in which language models
fine-tune with the corpus-specific tokenization instead of the default one,
which is being used during pretraining. This usually improves the resulting
performance of the model, and in the paper, we demonstrate that vocabulary
transfer is especially beneficial for medical text processing. Using three
different medical natural language processing datasets, we show vocabulary
transfer to provide up to ten extra percentage points for the downstream
classifier accuracy
Pragmatic Constraint on Distributional Semantics
This paper studies the limits of language models' statistical learning in the
context of Zipf's law. First, we demonstrate that Zipf-law token distribution
emerges irrespective of the chosen tokenization. Second, we show that Zipf
distribution is characterized by two distinct groups of tokens that differ both
in terms of their frequency and their semantics. Namely, the tokens that have a
one-to-one correspondence with one semantic concept have different statistical
properties than those with semantic ambiguity. Finally, we demonstrate how
these properties interfere with statistical learning procedures motivated by
distributional semantics