Legalese can often be filled with verbose domain-specific jargon which can make it challenging to understand and use for non-experts. Creating succinct summaries of legal documents often makes it easier for user comprehension. However, obtaining labeled data for every domain of legal text is challenging, which makes cross-domain transferability of text generation models for legal text, an important area of research. In this paper, we explore the ability of existing state-of-the-art T5 \& BART-based summarization models to transfer across legal domains. We leverage publicly available datasets across four domains for this task, one of which is a new resource for summarizing privacy policies, that we curate and release for academic research. Our experiments demonstrate the low cross-domain transferability of these models, while also highlighting the benefits of combining different domains. Further, we compare the effectiveness of standard metrics for this task and illustrate the vast differences in their performance.

Towards Cross-Domain Transferability of Text Generation Models for Legal Text

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever- aged similarly for all users’ queries during inference. However, such an assumption is not applicable to real-world settings, such as health- care, where one needs to handle queries of new users over unseen KGs during inference. Furthermore, privacy concerns and high computational costs render it infeasible to query the single KG that has information about all users while answering a specific user’s query. The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG. We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly. We propose two complementary approaches, PATHCBR and PATHRGCN for PERKGQA. The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks. Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.

PerKGQA: Question Answering over Personalized Knowledge Graphs

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is leveraged similarly for all users' queries during inference. However, such an assumption is not applicable to real-world settings, such as healthcare, where one needs to handle queries of new users over unseen KGs during inference. Furthermore, privacy concerns and high computational cost render it infeasible to query the single KG that has information of all users while answering a specific user's query. This motivates our setting of question answering over personalized knowledge graphs PerKGQA where each user has restricted access to their own KG. We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly. We propose two complementary approaches, PathCBR and PathRGCN, to tackle PerKGQA The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks. Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.

PERKGQA: Question Answering over Personalized Knowledge Graphs

A challenge for targeted sentiment analysis is that most datasets are domain-specific and thus building supervised models for a new target domain requires substantial annotation effort. Domain adaptation for this task has two dimensions: the nature of targets and the opinion words used to describe sentiment towards the target. We present a data sampling strategy informed by domain differences across these two dimensions with the goal of selecting a small number of examples, thereby minimizing annotation effort. This obtains performance in the 86-100% range compared to the full supervised model using only 4-15% of the full training data.

Sample Selection Guided by Domain and Task for Cross-Domain Targeted Sentiment Analysis

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer-grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multi-task framework. We provide a thorough analysis to gain insights into model performance.

Kasturi Bhattacharjee

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