805 research outputs found
Partial Learning Using Link Grammars Data
International audienceKanazawa has shown that several non-trivial classes of cate- gorial grammars are learnable in Gold's model. We propose in this article to adapt this kind of symbolic learning to natural languages. In order to compensate the combinatorial explosion of the learning algorithm, we suppose that a small part of the grammar to be learned is given as in- put. That is why we need some initial data to test the feasibility of the approach: link grammars are closely related to categorial grammars, and we use the English lexicon which exists in this formalism
Weakly Restricted Stochastic Grammars
A new type of stochastic grammars is introduced for investigation: weakly restricted stochastic grammars. In this paper we will concentrate on the consistency problem. To find conditions for stochastic grammars to be consistent, the theory of multitype Galton-Watson branching processes and generating functions is of central importance.\ud
The unrestricted stochastic grammar formalism generates the same class of languages as the weakly restricted formalism. The inside-outside algorithm is adapted for use with weakly restricted grammars
Faithful to the Original: Fact Aware Neural Abstractive Summarization
Unlike extractive summarization, abstractive summarization has to fuse
different parts of the source text, which inclines to create fake facts. Our
preliminary study reveals nearly 30% of the outputs from a state-of-the-art
neural summarization system suffer from this problem. While previous
abstractive summarization approaches usually focus on the improvement of
informativeness, we argue that faithfulness is also a vital prerequisite for a
practical abstractive summarization system. To avoid generating fake facts in a
summary, we leverage open information extraction and dependency parse
technologies to extract actual fact descriptions from the source text. The
dual-attention sequence-to-sequence framework is then proposed to force the
generation conditioned on both the source text and the extracted fact
descriptions. Experiments on the Gigaword benchmark dataset demonstrate that
our model can greatly reduce fake summaries by 80%. Notably, the fact
descriptions also bring significant improvement on informativeness since they
often condense the meaning of the source text.Comment: 8 pages, 3 figures, AAAI 201
LLM-Enhanced Data Management
Machine learning (ML) techniques for optimizing data management problems have
been extensively studied and widely deployed in recent five years. However
traditional ML methods have limitations on generalizability (adapting to
different scenarios) and inference ability (understanding the context).
Fortunately, large language models (LLMs) have shown high generalizability and
human-competitive abilities in understanding context, which are promising for
data management tasks (e.g., database diagnosis, database tuning). However,
existing LLMs have several limitations: hallucination, high cost, and low
accuracy for complicated tasks. To address these challenges, we design LLMDB,
an LLM-enhanced data management paradigm which has generalizability and high
inference ability while avoiding hallucination, reducing LLM cost, and
achieving high accuracy. LLMDB embeds domain-specific knowledge to avoid
hallucination by LLM fine-tuning and prompt engineering. LLMDB reduces the high
cost of LLMs by vector databases which provide semantic search and caching
abilities. LLMDB improves the task accuracy by LLM agent which provides
multiple-round inference and pipeline executions. We showcase three real-world
scenarios that LLMDB can well support, including query rewrite, database
diagnosis and data analytics. We also summarize the open research challenges of
LLMDB
CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania
The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science.
There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science.
This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible
Lassie: HOL4 Tactics by Example
Proof engineering efforts using interactive theorem proving have yielded
several impressive projects in software systems and mathematics. A key obstacle
to such efforts is the requirement that the domain expert is also an expert in
the low-level details in constructing the proof in a theorem prover. In
particular, the user needs to select a sequence of tactics that lead to a
successful proof, a task that in general requires knowledge of the exact names
and use of a large set of tactics.
We present Lassie, a tactic framework for the HOL4 theorem prover that allows
individual users to define their own tactic language by example and give
frequently used tactics or tactic combinations easier-to-remember names. The
core of Lassie is an extensible semantic parser, which allows the user to
interactively extend the tactic language through a process of definitional
generalization. Defining tactics in Lassie thus does not require any knowledge
in implementing custom tactics, while proofs written in Lassie retain the
correctness guarantees provided by the HOL4 system. We show through case
studies how Lassie can be used in small and larger proofs by novice and more
experienced interactive theorem prover users, and how we envision it to ease
the learning curve in a HOL4 tutorial
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