166,901 research outputs found
SemEval-2016 task 5 : aspect based sentiment analysis
International audienceThis paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams
Bilingually motivated domain-adapted word segmentation for statistical machine translation
We introduce a word segmentation approach to languages where word boundaries are not orthographically marked,
with application to Phrase-Based Statistical Machine Translation (PB-SMT). Instead of using manually segmented monolingual domain-specific corpora to train segmenters, we make use of bilingual corpora and statistical word alignment techniques. First of all, our approach is
adapted for the specific translation task at hand by taking the corresponding source (target) language into account. Secondly, this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains. We evaluate the performance of our segmentation approach on PB-SMT tasks from two domains and
demonstrate that our approach scores consistently among the best results across different data conditions
Towards Formal Interaction-Based Models of Grid Computing Infrastructures
Grid computing (GC) systems are large-scale virtual machines, built upon a
massive pool of resources (processing time, storage, software) that often span
multiple distributed domains. Concurrent users interact with the grid by adding
new tasks; the grid is expected to assign resources to tasks in a fair,
trustworthy way. These distinctive features of GC systems make their
specification and verification a challenging issue. Although prior works have
proposed formal approaches to the specification of GC systems, a precise
account of the interaction model which underlies resource sharing has not been
yet proposed. In this paper, we describe ongoing work aimed at filling in this
gap. Our approach relies on (higher-order) process calculi: these core
languages for concurrency offer a compositional framework in which GC systems
can be precisely described and potentially reasoned about.Comment: In Proceedings DCM 2013, arXiv:1403.768
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
LLM Augmented LLMs: Expanding Capabilities through Composition
Foundational models with billions of parameters which have been trained on
large corpora of data have demonstrated non-trivial skills in a variety of
domains. However, due to their monolithic structure, it is challenging and
expensive to augment them or impart new skills. On the other hand, due to their
adaptation abilities, several new instances of these models are being trained
towards new domains and tasks. In this work, we study the problem of efficient
and practical composition of existing foundation models with more specific
models to enable newer capabilities. To this end, we propose CALM --
Composition to Augment Language Models -- which introduces cross-attention
between models to compose their representations and enable new capabilities.
Salient features of CALM are: (i) Scales up LLMs on new tasks by 're-using'
existing LLMs along with a few additional parameters and data, (ii) Existing
model weights are kept intact, and hence preserves existing capabilities, and
(iii) Applies to diverse domains and settings. We illustrate that augmenting
PaLM2-S with a smaller model trained on low-resource languages results in an
absolute improvement of up to 13\% on tasks like translation into English and
arithmetic reasoning for low-resource languages. Similarly, when PaLM2-S is
augmented with a code-specific model, we see a relative improvement of 40\%
over the base model for code generation and explanation tasks -- on-par with
fully fine-tuned counterparts.Comment: 17 pages, 2 figures, 8 table
- âŠ