Gerasimos Lampouras


2024

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Proceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)
Yvette Graham | Qun Liu | Gerasimos Lampouras | Ignacio Iacobacci | Sinead Madden | Haider Khalid | Rameez Qureshi
Proceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)

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Findings of the First Workshop on Simulating Conversational Intelligence in Chat
Yvette Graham | Mohammed Rameez Qureshi | Haider Khalid | Gerasimos Lampouras | Ignacio Iacobacci | Qun Liu
Proceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)

The aim of this workshop is to bring together experts working on open-domain dialogue research. In this speedily advancing research area many challenges still exist, such as learning information from conversations, engaging in realistic and convincing simulation of human intelligence and reasoning. SCI-CHAT follows previous workshops on open domain dialogue but with a focus on the simulation of intelligent conversation as judged in a live human evaluation. Models aim to include the ability to follow a challenging topic over a multi-turn conversation, while positing, refuting and reasoning over arguments. The workshop included both a research track and shared task. The main goal of this paper is to provide an overview of the shared task and a link to an additional paper that will include an in depth analysis of the shared task results following presentation at the workshop.

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Text-to-Code Generation with Modality-relative Pre-training
Fenia Christopoulou | Guchun Zhang | Gerasimos Lampouras
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Large pre-trained language models have recently been expanded and applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model–where training sequences typically contain both natural and (linearised) programming language. Such approaches effectively map both modalities of the sequence into the same embedding space. However, programming language keywords (e.g. “while”) often have very strictly defined semantics. As such, transfer learning from their natural language usage may not necessarily be beneficial to their code application and vise versa. Assuming an already pre-trained language model, in this work we investigate how sequence tokens can be adapted and represented differently, depending on which modality they belong to, and to the ultimate benefit of the downstream task. We experiment with separating embedding spaces between modalities during further model pre-training with modality-relative training objectives. We focus on text-to-code generation and observe consistent improvements across two backbone models and two test sets, measuring pass@k and a novel incremental variation.

2023

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Exploring Data Augmentation for Code Generation Tasks
Pinzhen Chen | Gerasimos Lampouras
Findings of the Association for Computational Linguistics: EACL 2023

Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.

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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.

2022

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Hierarchical Recurrent Aggregative Generation for Few-Shot NLG
Giulio Zhou | Gerasimos Lampouras | Ignacio Iacobacci
Findings of the Association for Computational Linguistics: ACL 2022

Large pretrained models enable transfer learning to low-resource domains for language generation tasks. However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. To exploit these varying potentials for transfer learning, we propose a new hierarchical approach for few-shot and zero-shot generation. Our approach consists of a three-moduled jointly trained architecture: the first module independently lexicalises the distinct units of information in the input as sentence sub-units (e.g. phrases), the second module recurrently aggregates these sub-units to generate a unified intermediate output, while the third module subsequently post-edits it to generate a coherent and fluent final text. We perform extensive empirical analysis and ablation studies on few-shot and zero-shot settings across 4 datasets. Automatic and human evaluation shows that the proposed hierarchical approach is consistently capable of achieving state-of-the-art results when compared to previous work.

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Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access
Yue Feng | Gerasimos Lampouras | Ignacio Iacobacci
Findings of the Association for Computational Linguistics: EMNLP 2022

To alleviate the problem of structured databases’ limited coverage, recent task-oriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses. However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance. In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose “Topic-Aware Response Generation” (TARG), an end-to-end response generation model. TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history. Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.

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Proceedings of the Sixth Workshop on Structured Prediction for NLP
Andreas Vlachos | Priyanka Agrawal | André Martins | Gerasimos Lampouras | Chunchuan Lyu
Proceedings of the Sixth Workshop on Structured Prediction for NLP

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Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU
Fenia Christopoulou | Gerasimos Lampouras | Ignacio Iacobacci
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability. Current approaches for Natural Language Understanding (NLU) tasks use CL to improve in-distribution data performance often via heuristic-oriented or task-agnostic difficulties. In this work, instead, we employ CL for NLU by taking advantage of training dynamics as difficulty metrics, i.e., statistics that measure the behavior of the model at hand on specific task-data instances during training and propose modifications of existing CL schedulers based on these statistics. Differently from existing works, we focus on evaluating models on in-distribution (ID), out-of-distribution (OOD) as well as zero-shot (ZS) cross-lingual transfer datasets. We show across several NLU tasks that CL with training dynamics can result in better performance mostly on zero-shot cross-lingual transfer and OOD settings with improvements up by 8.5% in certain cases. Overall, experiments indicate that training dynamics can lead to better performing models with smoother training compared to other difficulty metrics while being 20% faster on average. In addition, through analysis we shed light on the correlations of task-specific versus task-agnostic metrics.

2021

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Informed Sampling for Diversity in Concept-to-Text NLG
Giulio Zhou | Gerasimos Lampouras
Findings of the Association for Computational Linguistics: EMNLP 2021

Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the output. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus our experiments on concept-to-text generation where models are sensitive to the inclusion of irrelevant words due to the strict relation between input and output. Our analysis shows that previous methods for diversity underperform in this setting, while human evaluation suggests that our proposed method achieves a high level of diversity with minimal effect on the output’s fluency and adequacy.

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Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation
Giulio Zhou | Gerasimos Lampouras
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Concept-to-text Natural Language Generation is the task of expressing an input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the input. However, this often requires that the input appears verbatim in the output text. This poses challenges in multilingual settings, where the task expands to generate the output text in multiple languages given the same input. In this paper, we explore the application of multilingual models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses multilingual pretrained embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our experiments across five datasets and five languages show that multilingual models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions.

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Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management
Milan Gritta | Gerasimos Lampouras | Ignacio Iacobacci
Transactions of the Association for Computational Linguistics, Volume 9

Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size con- sidering the complexity of the dialogues. Additionally, conventional training signal in- ference is not suitable for non-deterministic agent behavior, namely, considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non- deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.

2020

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WebNLG Challenge 2020: Language Agnostic Delexicalisation for Multilingual RDF-to-text generation
Giulio Zhou | Gerasimos Lampouras
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

This paper presents our submission to the WebNLG Challenge 2020 for the English and Russian RDF-to-text generation tasks. Our first of three submissions is based on Language Agnostic Delexicalisation, a novel delexicalisation method that match values in the input to their occurrences in the corresponding text through comparison of pretrained multilingual embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our second submission forfeits delexicalisation and uses SentencePiece subwords as basic units. Our third submission combines the previous two by alternating between the output of the delexicalisation-based system when the input contains unseen entities and/or properties and the output of the SentencePiece-based system when the input is seen during training.

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Proceedings of the Fourth Workshop on Structured Prediction for NLP
Priyanka Agrawal | Zornitsa Kozareva | Julia Kreutzer | Gerasimos Lampouras | André Martins | Sujith Ravi | Andreas Vlachos
Proceedings of the Fourth Workshop on Structured Prediction for NLP

2019

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Proceedings of the Third Workshop on Structured Prediction for NLP
Andre Martins | Andreas Vlachos | Zornitsa Kozareva | Sujith Ravi | Gerasimos Lampouras | Vlad Niculae | Julia Kreutzer
Proceedings of the Third Workshop on Structured Prediction for NLP

2017

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Imitation learning for structured prediction in natural language processing
Andreas Vlachos | Gerasimos Lampouras | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts

Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous flight from pilot demonstrations. Recently, algorithms for structured prediction were proposed under this paradigm and have been applied successfully to a number of tasks including syntactic dependency parsing, information extraction, coreference resolution, dynamic feature selection, semantic parsing and natural language generation. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. Our aim in this tutorial is to have a unified presentation of the various imitation algorithms for structure prediction, and show how they can be applied to a variety of NLP tasks.All material associated with the tutorial will be made available through https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017/.

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Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
Gerasimos Lampouras | Andreas Vlachos
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively.

2016

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Imitation learning for language generation from unaligned data
Gerasimos Lampouras | Andreas Vlachos
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Natural language generation (NLG) is the task of generating natural language from a meaning representation. Current rule-based approaches require domain-specific and manually constructed linguistic resources, while most machine-learning based approaches rely on aligned training data and/or phrase templates. The latter are needed to restrict the search space for the structured prediction task defined by the unaligned datasets. In this work we propose the use of imitation learning for structured prediction which learns an incremental model that handles the large search space by avoiding explicit enumeration of the outputs. We focus on the Locally Optimal Learning to Search framework which allows us to train against non-decomposable loss functions such as the BLEU or ROUGE scores while not assuming gold standard alignments. We evaluate our approach on three datasets using both automatic measures and human judgements and achieve results comparable to the state-of-the-art approaches developed for each of them.

2013

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Using Integer Linear Programming for Content Selection, Lexicalization, and Aggregation to Produce Compact Texts from OWL Ontologies
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 14th European Workshop on Natural Language Generation

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Using Integer Linear Programming in Concept-to-Text Generation to Produce More Compact Texts
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Extractive Multi-Document Summarization with Integer Linear Programming and Support Vector Regression
Dimitrios Galanis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of COLING 2012

2009

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Finding Short Definitions of Terms on Web Pages
Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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An Open-Source Natural Language Generator for OWL Ontologies and its Use in Protege and Second Life
Dimitrios Galanis | George Karakatsiotis | Gerasimos Lampouras | Ion Androutsopoulos
Proceedings of the Demonstrations Session at EACL 2009

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Adaptive Natural Language Interaction
Stasinos Konstantopoulos | Athanasios Tegos | Dimitrios Bilidas | Ion Androutsopoulos | Gerasimos Lampouras | Colin Matheson | Olivier Deroo | Prodromos Malakasiotis
Proceedings of the Demonstrations Session at EACL 2009