6,155 research outputs found
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Existing approaches to automatic VerbNet-style verb classification are
heavily dependent on feature engineering and therefore limited to languages
with mature NLP pipelines. In this work, we propose a novel cross-lingual
transfer method for inducing VerbNets for multiple languages. To the best of
our knowledge, this is the first study which demonstrates how the architectures
for learning word embeddings can be applied to this challenging
syntactic-semantic task. Our method uses cross-lingual translation pairs to tie
each of the six target languages into a bilingual vector space with English,
jointly specialising the representations to encode the relational information
from English VerbNet. A standard clustering algorithm is then run on top of the
VerbNet-specialised representations, using vector dimensions as features for
learning verb classes. Our results show that the proposed cross-lingual
transfer approach sets new state-of-the-art verb classification performance
across all six target languages explored in this work.Comment: EMNLP 2017 (long paper
Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
The state-of-the-art named entity recognition (NER) systems are supervised
machine learning models that require large amounts of manually annotated data
to achieve high accuracy. However, annotating NER data by human is expensive
and time-consuming, and can be quite difficult for a new language. In this
paper, we present two weakly supervised approaches for cross-lingual NER with
no human annotation in a target language. The first approach is to create
automatically labeled NER data for a target language via annotation projection
on comparable corpora, where we develop a heuristic scheme that effectively
selects good-quality projection-labeled data from noisy data. The second
approach is to project distributed representations of words (word embeddings)
from a target language to a source language, so that the source-language NER
system can be applied to the target language without re-training. We also
design two co-decoding schemes that effectively combine the outputs of the two
projection-based approaches. We evaluate the performance of the proposed
approaches on both in-house and open NER data for several target languages. The
results show that the combined systems outperform three other weakly supervised
approaches on the CoNLL data.Comment: 11 pages, The 55th Annual Meeting of the Association for
Computational Linguistics (ACL), 201
Contextual Label Projection for Cross-Lingual Structure Extraction
Translating training data into target languages has proven beneficial for
cross-lingual transfer. However, for structure extraction tasks, translating
data requires a label projection step, which translates input text and obtains
translated labels in the translated text jointly. Previous research in label
projection mostly compromises translation quality by either facilitating easy
identification of translated labels from translated text or using word-level
alignment between translation pairs to assemble translated phrase-level labels
from the aligned words. In this paper, we introduce CLAP, which first
translates text to the target language and performs contextual translation on
the labels using the translated text as the context, ensuring better accuracy
for the translated labels. We leverage instruction-tuned language models with
multilingual capabilities as our contextual translator, imposing the constraint
of the presence of translated labels in the translated text via instructions.
We compare CLAP with other label projection techniques for creating
pseudo-training data in target languages on event argument extraction, a
representative structure extraction task. Results show that CLAP improves by
2-2.5 F1-score over other methods on the Chinese and Arabic ACE05 datasets.Comment: Work in Progres
Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction
Latest efforts on cross-lingual relation extraction (XRE) aggressively
leverage the language-consistent structural features from the universal
dependency (UD) resource, while they may largely suffer from biased transfer
(e.g., either target-biased or source-biased) due to the inevitable linguistic
disparity between languages. In this work, we investigate an unbiased UD-based
XRE transfer by constructing a type of code-mixed UD forest. We first translate
the sentence of the source language to the parallel target-side language, for
both of which we parse the UD tree respectively. Then, we merge the
source-/target-side UD structures as a unified code-mixed UD forest. With such
forest features, the gaps of UD-based XRE between the training and predicting
phases can be effectively closed. We conduct experiments on the ACE XRE
benchmark datasets, where the results demonstrate that the proposed code-mixed
UD forests help unbiased UD-based XRE transfer, with which we achieve
significant XRE performance gains
Event Extraction: A Survey
Extracting the reported events from text is one of the key research themes in
natural language processing. This process includes several tasks such as event
detection, argument extraction, role labeling. As one of the most important
topics in natural language processing and natural language understanding, the
applications of event extraction spans across a wide range of domains such as
newswire, biomedical domain, history and humanity, and cyber security. This
report presents a comprehensive survey for event detection from textual
documents. In this report, we provide the task definition, the evaluation
method, as well as the benchmark datasets and a taxonomy of methodologies for
event extraction. We also present our vision of future research direction in
event detection.Comment: 20 page
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Cross-lingual open information extraction aims to extract structured
information from raw text across multiple languages. Previous work uses a
shared cross-lingual pre-trained model to handle the different languages but
underuses the potential of the language-specific representation. In this paper,
we propose an effective multi-stage tuning framework called MT4CrossIE,
designed for enhancing cross-lingual open information extraction by injecting
language-specific knowledge into the shared model. Specifically, the
cross-lingual pre-trained model is first tuned in a shared semantic space
(e.g., embedding matrix) in the fixed encoder and then other components are
optimized in the second stage. After enough training, we freeze the pre-trained
model and tune the multiple extra low-rank language-specific modules using
mixture-of-LoRAs for model-based cross-lingual transfer. In addition, we
leverage two-stage prompting to encourage the large language model (LLM) to
annotate the multi-lingual raw data for data-based cross-lingual transfer. The
model is trained with multi-lingual objectives on our proposed dataset
OpenIE4++ by combing the model-based and data-based transfer techniques.
Experimental results on various benchmarks emphasize the importance of
aggregating multiple plug-in-and-play language-specific modules and demonstrate
the effectiveness of MT4CrossIE in cross-lingual
OIE\footnote{\url{https://github.com/CSJianYang/Multilingual-Multimodal-NLP}}.Comment: 10 page
- …