Gaurav Singh Tomar
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Knowledge-grounded dialogue generation is a challenging task because it requires satisfying two fundamental yet often competing constraints: being responsive in a manner that is specific to what the conversation partner has said while also being attributable to an underlying source document. In this work, we bring this trade-off between these two objectives (specificity and attribution) to light and ask the question: Can explicit content planning before the response generation help the model to address this challenge? To answer this question, we design a framework called PLEDGE, which allows us to experiment with various plan variables explored in prior work, supporting both metric-agnostic and metric-aware approaches. While content planning shows promise, our results on whether it can actually help to navigate this trade-off are mixed -- planning mechanisms that are metric-aware (use automatic metrics during training) are better at automatic evaluations but underperform in human judgment compared to metric-agnostic mechanisms. We discuss how this may be caused by over-fitting to automatic metrics and the need for future work to better calibrate these metrics towards human judgment. We hope the observations from our analysis will inform future work that aims to apply content planning in this context.
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Measuring Attribution in Natural Language Generation Models
Iulia Turc
Computational Linguistics, 49 (2023), pp. 777-840
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With recent improvements in natural language generation (NLG) models for various applications, it has become imperative to have the means to identify and evaluate whether NLG output is only sharing verifiable information about the external world. In this work, we present a new evaluation framework entitled Attributable to Identified Sources (AIS) for assessing the output of natural language generation models, when such output pertains to the external world. We first define AIS and introduce a two-stage annotation pipeline for allowing annotators to appropriately evaluate model output according to AIS guidelines. We empirically validate this approach on generation datasets spanning three tasks (two conversational QA datasets, a summarization dataset, and a table-to-text dataset) via human evaluation studies that suggest that AIS could serve as a common framework for measuring whether model-generated statements are supported by underlying sources. We release guidelines for the human evaluation studies.
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CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Ellen Wu
Yi Luan
Hannaneh Hajishirzi
Mari Ostendorf
The 2022 Conference on Empirical Methods in Natural Language Processing (2022)
Preview abstract
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context. Moreover, it can be expensive to re-train well-established retrievers such as search engines that are originally developed for non-conversational queries. To facilitate their use, we develop a query rewriting model CONQRR that rewrites a conversational question in the context into a standalone question. It is trained with a novel reward function to directly optimize towards retrieval using reinforcement learning and can be adapted to any off-the-shelf retriever. CONQRR achieves state-of-the-art results on a recent open-domain CQA dataset containing conversations from three different sources, and is effective for two different off-the-shelf retrievers. Our extensive analysis also shows the robustness of CONQRR to out-of-domain dialogues as well as to zero query rewriting supervision.
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Dungeons and Dragons as a Challenge Problem for Artificial Intelligence
Chris Callison-Burch
Lara Martin
NAACL Wordplay Workshop, ACL (2022) (to appear)
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AI researchers have posited Dungeons and Dragons (D&D) as a challenge problem to test systems on various language-related capabilities. In this paper, we frame D&D specifically as a dialogue system challenge, where the tasks are to both generate the next conversational turn in the game and predict the state of the game given the dialogue history. We create a gameplay dataset consisting of nearly 900 games, with a total of 7,000 players, 800,000 dialogue turns, 500,000 dice rolls, and 58 million words. We automatically annotate the data with partial state information about the game play. We train a large language model to generate the next game turn, conditioning it on different information. The LM can respond as a particular character or as the player who runs the game—i.e., the Dungeon Master (DM). It is trained to produce dialogue that is either in-character (roleplaying in the fictional world) or out-of-character (discussing rules or strategy). We perform a human evaluation to determine what factors make the generated output plausible and interesting. We further perform an automatic evaluation to determine how well the model can predict the game state given the history and examine how well tracking the game state improves its ability to produce plausible conversational output.
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Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features
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) (2021), pp. 704-718
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Knowledge-grounded dialogue systems are intended to convey information that is based on evidence provided in a given source text. We discuss the challenges of training a generative neural dialogue model for such systems that is controlled to stay faithful to the evidence. Existing datasets contain a mix of conversational responses that are faithful to selected evidence as well as more subjective or chit-chat style responses. We propose different evaluation measures to disentangle these different styles of responses by quantifying the informativeness and objectivity. At training time, additional inputs based on these evaluation measures are given to the dialogue model. At generation time, these additional inputs act as stylistic controls that encourage the model to generate responses that are faithful to the provided evidence. We also investigate the usage of additional controls at decoding time using resampling techniques. In addition to automatic metrics, we perform a human evaluation study where raters judge the output of these controlled generation models to be generally more objective and faithful to the evidence compared to baseline dialogue systems.
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We study the problem of model extraction in natural language processing, where an adversary with query access to a victim model attempts to reconstruct a local copy of the model. We show that when both the adversary and victim model fine-tune existing pretrained models such as BERT, the adversary does not need to have access to any training data to mount the attack. Indeed, we show that randomly sampled sequences of words, which do not satisfy grammar structures, make effective queries to extract textual models. This is true even for complex tasks such as natural language inference or question answering.
Our attacks can be mounted with a modest query budget of less than $400.The extraction's accuracy can be further improved using a large textual corpus like Wikipedia, or with intuitive heuristics we introduce. Finally, we measure the effectiveness of two potential defense strategies---membership classification and API watermarking. While these defenses mitigate certain adversaries and come at a low overhead because they do not require re-training of the victim model, fully coping with model extraction remains an open problem.
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Neural Paraphrase Identification of Questions with Noisy Pretraining
Thyago Duque
Oscar Täckström
Jakob Uszkoreit
Proceedings of the First Workshop on Subword and Character Level Models in NLP (2017)
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We present a solution to the problem of paraphrase identification of questions. We
focus on a recent dataset of question pairs annotated with binary paraphrase labels and
show that a variant of the decomposable attention model (Parikh et al., 2016) results in
accurate performance on this task, while being far simpler than many competing neural
architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically
collected question paraphrases, it obtains the best reported performance on the dataset.
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