48 research outputs found
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
We introduce INSTRUCTOR, a new method for computing text embeddings given
task instructions: every text input is embedded together with instructions
explaining the use case (e.g., task and domain descriptions). Unlike encoders
from prior work that are more specialized, INSTRUCTOR is a single embedder that
can generate text embeddings tailored to different downstream tasks and
domains, without any further training. We first annotate instructions for 330
diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive
loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are
unseen during training), ranging from classification and information retrieval
to semantic textual similarity and text generation evaluation. INSTRUCTOR,
while having an order of magnitude fewer parameters than the previous best
model, achieves state-of-the-art performance, with an average improvement of
3.4% compared to the previous best results on the 70 diverse datasets. Our
analysis suggests that INSTRUCTOR is robust to changes in instructions, and
that instruction finetuning mitigates the challenge of training a single model
on diverse datasets. Our model, code, and data are available at
https://instructor-embedding.github.io.Comment: Accepted in ACL2023 Finding
Deobfuscating Name Scrambling as a Natural Language Generation Task
We are interested in data-driven approaches to Natural Language Generation, but semantic representations for human text are difficult and expensive to construct. By considering a methods implementation as weak semantics for the English terms extracted from the method’s name we can collect massive datasets, akin to have words and sensor data aligned at a scale never seen before. We applied our learned model to name scrambling, a common technique used to protect intellectual property and increase the effort necessary to reverse engineer Java binary code: replacing all the method and class names by a random identifier.
Using 5.6M bytecode-compiled Java methods obtained from the Debian archive, we trained a Random Forest model to predict the first term in the method name. As features, we use primarily the opcodes of the bytecodes (that is, bytecodes without any parameters). Our results indicate that we can distinguish the 15 most popular terms from the others at 78% recall, helping a programmer performing reverse engineering to reduce half of the methods in a program they should further investigate.Sociedad Argentina de Informática e Investigación Operativ
Deobfuscating Name Scrambling as a Natural Language Generation Task
We are interested in data-driven approaches to Natural Language Generation, but semantic representations for human text are difficult and expensive to construct. By considering a methods implementation as weak semantics for the English terms extracted from the method’s name we can collect massive datasets, akin to have words and sensor data aligned at a scale never seen before. We applied our learned model to name scrambling, a common technique used to protect intellectual property and increase the effort necessary to reverse engineer Java binary code: replacing all the method and class names by a random identifier.
Using 5.6M bytecode-compiled Java methods obtained from the Debian archive, we trained a Random Forest model to predict the first term in the method name. As features, we use primarily the opcodes of the bytecodes (that is, bytecodes without any parameters). Our results indicate that we can distinguish the 15 most popular terms from the others at 78% recall, helping a programmer performing reverse engineering to reduce half of the methods in a program they should further investigate.Sociedad Argentina de Informática e Investigación Operativ
Deobfuscating Name Scrambling as a Natural Language Generation Task
We are interested in data-driven approaches to Natural Language Generation, but semantic representations for human text are difficult and expensive to construct. By considering a methods implementation as weak semantics for the English terms extracted from the method’s name we can collect massive datasets, akin to have words and sensor data aligned at a scale never seen before. We applied our learned model to name scrambling, a common technique used to protect intellectual property and increase the effort necessary to reverse engineer Java binary code: replacing all the method and class names by a random identifier.
Using 5.6M bytecode-compiled Java methods obtained from the Debian archive, we trained a Random Forest model to predict the first term in the method name. As features, we use primarily the opcodes of the bytecodes (that is, bytecodes without any parameters). Our results indicate that we can distinguish the 15 most popular terms from the others at 78% recall, helping a programmer performing reverse engineering to reduce half of the methods in a program they should further investigate.Sociedad Argentina de Informática e Investigación Operativ
Measuring associational thinking through word embeddings
[EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients.s Financial support for this research has been provided by the Spanish Ministry of
Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal
de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the
European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project
SMARTLAGOON].
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-62065210255
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?
Recent research on dialogue state tracking (DST) focuses on methods that
allow few- and zero-shot transfer to new domains or schemas. However,
performance gains heavily depend on aggressive data augmentation and
fine-tuning of ever larger language model based architectures. In contrast,
general purpose language models, trained on large amounts of diverse data, hold
the promise of solving any kind of task without task-specific training. We
present preliminary experimental results on the ChatGPT research preview,
showing that ChatGPT achieves state-of-the-art performance in zero-shot DST.
Despite our findings, we argue that properties inherent to general purpose
models limit their ability to replace specialized systems. We further theorize
that the in-context learning capabilities of such models will likely become
powerful tools to support the development of dedicated and dynamic dialogue
state trackers.Comment: 13 pages, 3 figures, accepted at ACL 202
Deobfuscating Name Scrambling as a Natural Language Generation Task
We are interested in data-driven approaches to Natural Language Generation, but semantic representations for human text are difficult and expensive to construct. By considering a methods implementation as weak semantics for the English terms extracted from the method’s name we can collect massive datasets, akin to have words and sensor dataaligned at a scale never seen before. We applied our learned model to name scrambling, a common technique used to protect intellectual property and increase the effort necessary to reverse engineer Java binary code: replacing all the method and class names by a random identifier. Using 5.6M bytecode-compiled Java methods obtained from the Debianarchive, we trained a Random Forest model to predict the first term in the method name. As features, we use primarily the opcodes of the bytecodes (that is, bytecodes without any parameters). Our results indicate that we can distinguish the 15 most popular terms from the others at 78% recall, helping a programmer performing reverse engineering to reduce half of the methods in a program they should further investigate. We also performed some preliminary experiments using neural machine translation.Special Issue dedicated to JAIIO 2018 (Jornadas Argentinas de Informática).Sociedad Argentina de Informática e Investigación Operativ
Le système de question-réponse QUANTUM
Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal