33,721 research outputs found
The Automation of Legal Reasoning: Customized AI Techniques for the Patent Field
As Artificial Intelligence and Machine Learning continue to transform numerous aspects of our everyday lives, their role in the legal profession is growing in prominence. A subfield of Al with particular applicability to legal analysis is Natural Language Processing (NLP). NLP deals with computational techniques for processing human languages such as English, making it a natural tool for processing the text of statutes, regulations, judicial decisions, contracts, and other legal instruments. Paradoxically, although state-of-the-art Machine Learning and NLP algorithms are able to learn and act upon patterns too complex for humans to perceive, they nevertheless perform poorly on many cognitive tasks that humans routinely perform effortlessly. This profoundly limits the ability of Al to assist in many forms of legal analysis and legal decision making.
This article offers two theses. First, notwithstanding impressive progress on NLP tasks in recent years, the state-of-the-art in NLP will remain unable to perform legal analysis for some time. Second, lawyers, legal scholars, and other domain experts can play an integral role in designing Al software that can partially automate legal analysis, overcoming some of the limitations in NLP capabilities
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Machine Learning Models for Efficient and Robust Natural Language Processing
Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks.
First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Assessing the Ability of Self-Attention Networks to Learn Word Order
Self-attention networks (SAN) have attracted a lot of interests due to their
high parallelization and strong performance on a variety of NLP tasks, e.g.
machine translation. Due to the lack of recurrence structure such as recurrent
neural networks (RNN), SAN is ascribed to be weak at learning positional
information of words for sequence modeling. However, neither this speculation
has been empirically confirmed, nor explanations for their strong performances
on machine translation tasks when "lacking positional information" have been
explored. To this end, we propose a novel word reordering detection task to
quantify how well the word order information learned by SAN and RNN.
Specifically, we randomly move one word to another position, and examine
whether a trained model can detect both the original and inserted positions.
Experimental results reveal that: 1) SAN trained on word reordering detection
indeed has difficulty learning the positional information even with the
position embedding; and 2) SAN trained on machine translation learns better
positional information than its RNN counterpart, in which position embedding
plays a critical role. Although recurrence structure make the model more
universally-effective on learning word order, learning objectives matter more
in the downstream tasks such as machine translation.Comment: ACL 201
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