6 research outputs found
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
Reviewing and comprehending key obligations, entitlements, and prohibitions
in legal contracts can be a tedious task due to their length and
domain-specificity. Furthermore, the key rights and duties requiring review
vary for each contracting party. In this work, we propose a new task of
party-specific extractive summarization for legal contracts to facilitate
faster reviewing and improved comprehension of rights and duties. To facilitate
this, we curate a dataset comprising of party-specific pairwise importance
comparisons annotated by legal experts, covering ~293K sentence pairs that
include obligations, entitlements, and prohibitions extracted from lease
agreements. Using this dataset, we train a pairwise importance ranker and
propose a pipeline-based extractive summarization system that generates a
party-specific contract summary. We establish the need for incorporating
domain-specific notion of importance during summarization by comparing our
system against various baselines using both automatic and human evaluation
methodsComment: EMNLP 202
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection
We present a novel method, SALAD, for the challenging vision task of adapting
a pre-trained "source" domain network to a "target" domain, with a small budget
for annotation in the "target" domain and a shift in the label space. Further,
the task assumes that the source data is not available for adaptation, due to
privacy concerns or otherwise. We postulate that such systems need to jointly
optimize the dual task of (i) selecting fixed number of samples from the target
domain for annotation and (ii) transfer of knowledge from the pre-trained
network to the target domain. To do this, SALAD consists of a novel Guided
Attention Transfer Network (GATN) and an active learning function, HAL. The
GATN enables feature distillation from pre-trained network to the target
network, complemented with the target samples mined by HAL using
transfer-ability and uncertainty criteria. SALAD has three key benefits: (i) it
is task-agnostic, and can be applied across various visual tasks such as
classification, segmentation and detection; (ii) it can handle shifts in output
label space from the pre-trained source network to the target domain; (iii) it
does not require access to source data for adaptation. We conduct extensive
experiments across 3 visual tasks, viz. digits classification (MNIST, SVHN,
VISDA), synthetic (GTA5) to real (CityScapes) image segmentation, and document
layout detection (PubLayNet to DSSE). We show that our source-free approach,
SALAD, results in an improvement of 0.5%-31.3%(across datasets and tasks) over
prior adaptation methods that assume access to large amounts of annotated
source data for adaptation
Entailment Relation Aware Paraphrase Generation
We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e.g. equivalent, forward entailing, or reverse entailing) with respect to a given
input. We propose a reinforcement learning-based weakly-supervised paraphrasing system, ERAP, that can be trained using existing paraphrase and natural language inference (NLI) corpora without an explicit task-specific corpus. A combination of automated and human evaluations show that ERAP generates paraphrases conforming to the specified entailment relation and are of good quality as compared to the baselines and uncontrolled paraphrasing systems. Using ERAP for augmenting training data for downstream textual entailment task improves performance over an uncontrolled paraphrasing system, and introduces fewer training artifacts, indicating the benefit of explicit control during paraphrasing