Sashank Santhanam


2021

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The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann | Tosin Adewumi | Karmanya Aggarwal | Pawan Sasanka Ammanamanchi | Anuoluwapo Aremu | Antoine Bosselut | Khyathi Raghavi Chandu | Miruna-Adriana Clinciu | Dipanjan Das | Kaustubh Dhole | Wanyu Du | Esin Durmus | Ondřej Dušek | Chris Chinenye Emezue | Varun Gangal | Cristina Garbacea | Tatsunori Hashimoto | Yufang Hou | Yacine Jernite | Harsh Jhamtani | Yangfeng Ji | Shailza Jolly | Mihir Kale | Dhruv Kumar | Faisal Ladhak | Aman Madaan | Mounica Maddela | Khyati Mahajan | Saad Mahamood | Bodhisattwa Prasad Majumder | Pedro Henrique Martins | Angelina McMillan-Major | Simon Mille | Emiel van Miltenburg | Moin Nadeem | Shashi Narayan | Vitaly Nikolaev | Andre Niyongabo Rubungo | Salomey Osei | Ankur Parikh | Laura Perez-Beltrachini | Niranjan Ramesh Rao | Vikas Raunak | Juan Diego Rodriguez | Sashank Santhanam | João Sedoc | Thibault Sellam | Samira Shaikh | Anastasia Shimorina | Marco Antonio Sobrevilla Cabezudo | Hendrik Strobelt | Nishant Subramani | Wei Xu | Diyi Yang | Akhila Yerukola | Jiawei Zhou
Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for the 2021 shared task at the associated GEM Workshop.

2020

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Active Defense Against Social Engineering: The Case for Human Language Technology
Adam Dalton | Ehsan Aghaei | Ehab Al-Shaer | Archna Bhatia | Esteban Castillo | Zhuo Cheng | Sreekar Dhaduvai | Qi Duan | Bryanna Hebenstreit | Md Mazharul Islam | Younes Karimi | Amir Masoumzadeh | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We describe a system that supports natural language processing (NLP) components for active defenses against social engineering attacks. We deploy a pipeline of human language technology, including Ask and Framing Detection, Named Entity Recognition, Dialogue Engineering, and Stylometry. The system processes modern message formats through a plug-in architecture to accommodate innovative approaches for message analysis, knowledge representation and dialogue generation. The novelty of the system is that it uses NLP for cyber defense and engages the attacker using bots to elicit evidence to attribute to the attacker and to waste the attacker’s time and resources.

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Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation
Archna Bhatia | Adam Dalton | Brodie Mather | Sashank Santhanam | Samira Shaikh | Alan Zemel | Tomek Strzalkowski | Bonnie J. Dorr
Proceedings for the First International Workshop on Social Threats in Online Conversations: Understanding and Management

We present a paradigm for extensible lexicon development based on Lexical Conceptual Structure to support social engineering detection and response generation. We leverage the central notions of ask (elicitation of behaviors such as providing access to money) and framing (risk/reward implied by the ask). We demonstrate improvements in ask/framing detection through refinements to our lexical organization and show that response generation qualitatively improves as ask/framing detection performance improves. The paradigm presents a systematic and efficient approach to resource adaptation for improved task-specific performance.

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Proceedings of the 1st Workshop on Evaluating NLG Evaluation
Shubham Agarwal | Ondřej Dušek | Sebastian Gehrmann | Dimitra Gkatzia | Ioannis Konstas | Emiel Van Miltenburg | Sashank Santhanam
Proceedings of the 1st Workshop on Evaluating NLG Evaluation

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Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions
David M. Howcroft | Anya Belz | Miruna-Adriana Clinciu | Dimitra Gkatzia | Sadid A. Hasan | Saad Mahamood | Simon Mille | Emiel van Miltenburg | Sashank Santhanam | Verena Rieser
Proceedings of the 13th International Conference on Natural Language Generation

Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adverse implications for meta-evaluation and reproducibility. In this paper, we present (i) our dataset of 165 NLG papers with human evaluations, (ii) the annotation scheme we developed to label the papers for different aspects of evaluations, (iii) quantitative analyses of the annotations, and (iv) a set of recommendations for improving standards in evaluation reporting. We use the annotations as a basis for examining information included in evaluation reports, and levels of consistency in approaches, experimental design and terminology, focusing in particular on the 200+ different terms that have been used for evaluated aspects of quality. We conclude that due to a pervasive lack of clarity in reports and extreme diversity in approaches, human evaluation in NLG presents as extremely confused in 2020, and that the field is in urgent need of standard methods and terminology.

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Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
Sashank Santhanam | Zhuo Cheng | Brodie Mather | Bonnie Dorr | Archna Bhatia | Bryanna Hebenstreit | Alan Zemel | Adam Dalton | Tomek Strzalkowski | Samira Shaikh
Findings of the Association for Computational Linguistics: EMNLP 2020

Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.

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Understanding the Impact of Experiment Design for Evaluating Dialogue System Output
Sashank Santhanam | Samira Shaikh
Proceedings of the The Fourth Widening Natural Language Processing Workshop

Evaluation of output from natural language generation (NLG) systems is typically conducted via crowdsourced human judgments. To understand the impact of how experiment design might affect the quality and consistency of such human judgments, we designed a between-subjects study with four experimental conditions. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as no prior experience of participating in similar studies of rating dialogue system output

2019

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Towards Best Experiment Design for Evaluating Dialogue System Output
Sashank Santhanam | Samira Shaikh
Proceedings of the 12th International Conference on Natural Language Generation

To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence. While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design of the evaluation task affects the consistency and quality of human judgments. We conduct a between-subjects study to understand the impact of four experiment conditions on human ratings of dialogue system output. In addition to discrete and continuous scale ratings, we also experiment with a novel application of Best-Worst scaling to dialogue evaluation. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as time taken to complete the task and no prior experience of participating in similar studies of rating dialogue system output positively impact consistency and agreement amongst raters.

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Emotional Neural Language Generation Grounded in Situational Contexts
Sashank Santhanam | Samira Shaikh
Proceedings of the 4th Workshop on Computational Creativity in Language Generation