Philip Gorinski

Also published as: Philip John Gorinski


2023

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Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis
Philip Gorinski | Matthieu Zimmer | Gerasimos Lampouras | Derrick Goh Xin Deik | Ignacio Iacobacci
Findings of the Association for Computational Linguistics: EMNLP 2023

The advent of large pre-trained language models in the domain of Code Synthesis has shown remarkable performance on various benchmarks, treating the problem of Code Generation in a fashion similar to Natural Language Generation, trained with a Language Modelling (LM) objective. In addition, the property of programming language code being precisely evaluable with respect to its semantics – through the use of Unit Tests to check its functional correctness – lends itself to using Reinforcement Learning (RL) as a further training paradigm. Previous work has shown that RL can be applied as such to improve models’ coding capabilities; however, such RL-based methods rely on a reward signal based on defined Unit Tests, which are much harder to obtain compared to the huge crawled code datasets used in LM objectives. In this work, we present a novel approach to automatically obtain data consisting of function signatures and associated Unit Tests, suitable for RL training of Code Synthesis models. We also introduce a straightforward, simple yet effective Actor-Critic RL training scheme and show that it, in conjunction with automatically generated training data, leads to improvement of a pre-trained code language model’s performance by up to 9.9% improvement over the original underlying code synthesis LM, and up to 4.3% over RL-based models trained with standard PPO or CodeRL.

2020

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Learning Dialog Policies from Weak Demonstrations
Gabriel Gordon-Hall | Philip John Gorinski | Shay B. Cohen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems. Building upon Deep Q-learning from Demonstrations (DQfD), an algorithm that scores highly in difficult Atari games, we leverage dialog data to guide the agent to successfully respond to a user’s requests. We make progressively fewer assumptions about the data needed, using labeled, reduced-labeled, and even unlabeled data to train expert demonstrators. We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment. Experiments in a challenging multi-domain dialog system framework validate our approaches, and get high success rates even when trained on out-of-domain data.

2018

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What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis
Philip John Gorinski | Mirella Lapata
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This work takes a first step toward movie content analysis by tackling the novel task of movie overview generation. Overviews are natural language texts that give a first impression of a movie, describing aspects such as its genre, plot, mood, or artistic style. We create a dataset that consists of movie scripts, attribute-value pairs for the movies’ aspects, as well as overviews, which we extract from an online database. We present a novel end-to-end model for overview generation, consisting of a multi-label encoder for identifying screenplay attributes, and an LSTM decoder to generate natural language sentences conditioned on the identified attributes. Automatic and human evaluation show that the encoder is able to reliably assign good labels for the movie’s attributes, and the overviews provide descriptions of the movie’s content which are informative and faithful.

2015

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Movie Script Summarization as Graph-based Scene Extraction
Philip John Gorinski | Mirella Lapata
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

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Towards Weakly Supervised Resolution of Null Instantiations
Philip Gorinski | Josef Ruppenhofer | Caroline Sporleder
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

2012

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Syntactic Surprisal Affects Spoken Word Duration in Conversational Contexts
Vera Demberg | Asad Sayeed | Philip Gorinski | Nikolaos Engonopoulos
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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In Search of Missing Arguments: A Linguistic Approach
Josef Ruppenhofer | Philip Gorinski | Caroline Sporleder
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Idioms in Context: The IDIX Corpus
Caroline Sporleder | Linlin Li | Philip Gorinski | Xaver Koch
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Idioms and other figuratively used expressions pose considerable problems to natural language processing applications because they are very frequent and often behave idiosyncratically. Consequently, there has been much research on the automatic detection and extraction of idiomatic expressions. Most studies focus on type-based idiom detection, i.e., distinguishing whether a given expression can (potentially) be used idiomatically. However, many expressions such as ""break the ice"" can have both literal and non-literal readings and need to be disambiguated in a given context (token-based detection). So far relatively few approaches have attempted context-based idiom detection. One reason for this may be that few annotated resources are available that disambiguate expressions in context. With the IDIX corpus, we aim to address this. IDIX is available as an add-on to the BNC and disambiguates different usages of a subset of idioms. We believe that this resource will be useful both for linguistic and computational linguistic studies.