A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper explores zero-shot cross-lingual reading comprehension using pre-trained multi-lingual model.
Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e…
Paper refines cross-lingual word embeddings using Manhattan norm.
\emph{Sentiment Quantification} (i.e., the task of estimating the relative frequency of sentiment-related classes -- such as \textsf{Positive} and \textsf{Negative} -- in a set of unlabelled documents) is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a populati…
The authors advocate for more rigorous unsupervised cross-lingual learning methods.
Paper develops a new unsupervised scoring function for cross-lingual document alignment.
CCAligned creates a massive web document dataset for cross-lingual research.
Develops a cross-lingual hate speech detection model using pre-trained Transformers.
Study uncovers financial trends from cross-lingual news data.
This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python. DCI is a transfer learning method for cross-domain and cross-lingual text classification for which we had provided an implementation (here called JaDCI) built on top of JaTeCS, a Java framework for text …
In this work, we train fully convolutional networks to detect anger in speech. Since training these deep architectures requires large amounts of data and the size of emotion datasets is relatively small, we use transfer learning. However, unlike previous approaches that use speech or emotion-based tasks for the source …
BERT model improves cross-lingual document retrieval.
Paper reproduces and enhances a method for cross-lingual word embeddings.
LPL optimizes embeddings to align local neighborhoods, improving cross-lingual word alignment.
Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when naively classifying each document via its corresponding language-specific classifier. In order to obtain an…
NMIXX fine-tunes embeddings for finance, outperforming general models in Korean.
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual dat…
Although over 100 languages are supported by strong off-the-shelf machine translation systems, only a subset of them possess large annotated corpora for named entity recognition. Motivated by this fact, we leverage machine translation to improve annotation-projection approaches to cross-lingual named entity recognition…
We solve a key problem in cross-lingual learning using a novel approach.
Simple framework decouples word alignment and multilingual embedding mapping.
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover's Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be ex…
Introduces PELP for graph-enhanced word embeddings.
Neural language modeling (LM) has led to significant improvements in several applications, including Automatic Speech Recognition. However, they typically require large amounts of training data, which is not available for many domains and languages. In this study, we propose a multilingual neural language model archite…
Triangulation filters spurious circuits in multilingual models.
HMS-BERT detects cyberbullying in multiple languages and labels.
Study quantifies gender bias in language models across 7 languages.
A framework for multi-label sentiment analysis in 100 languages with dynamic weighting.
The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relativ…
We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logge…
Unsupervised machine translation---i.e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently proposed a fully unsupervised machine translation (MT) model. The model relies heavily on an adversari…
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel m…
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our new methodologies for mining such data from previously obtained comparable corpora. The task is highly practical since non-para…
We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples learning the transformation from the source language to the target language into (a) learning rotations for language-specific embeddings to align them to a common space, …
Transfer learning improves portfolio optimization by identifying transfer risk.
Paper analyzes transfer risk in transfer learning for finance.
L-Cool improves image and language translation by cooling low-density samples.
This paper explores the connection between adversarial and knowledge transferability.
The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates th…
Mathematical framework for transfer learning feasibility and transfer risk.
Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To dis…
Survey connects and systematizes transfer learning research.
Study measures impact of data and neural net similarity on transferability in restaurant sales data.
The paper analyzes phase transitions in transfer learning for perceptrons.
A novel OT-based method for aligning hyperbolic representations.
Adaptive source selection for positive transfer in linear models improves target dataset performance.
EvoGrad improves efficiency in meta-learning and hyperparameter optimization.
New research on limits of transfer learning, proving key selection and dependence requirements.
Proposes a transfer learning method for high-dimensional quantile regression.