Large Language Models (LLMs) are revolutionizing conversational recommender systems (CRS) by effectively indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, the autoregressive nature of LLMs, which outputs item titles as a long sequence of subtokens, hinders the ability to efficiently obtain and control recommendations across the entire item set. This challenge in calculating probabilities over all items limits LLMs' potential, such as (1) limiting control over recommendation popularities and (2) preventing the synergy of marrying LLMs and traditional recommender systems (RecSys).

Conversational recommender systems (CRS) should understand users’ expressed interests, which are frequently semantically rich and knowledge-intensive. Prior works attempt to address this challenge by using external knowledge bases or parametric knowledge in large language models (LLMs). In this paper, we study a complementary solution, exploiting item knowledge in the training data. We hypothesize that many inference-time user requests can be answered by reusing popular crowd-written answers associated with similar training queries. Following this intuition, we define a class of neighborhood-based CRS that makes recommendations by identifying items commonly associated with similar training dialogue contexts. Experiments on Inspired, Redial, and Reddit-Movie benchmarks show our method outperforms state-of-the-art LLMs with 2 billion parameters, and offers on-par performance to 7 billion parameter models while using over 170 times less GPU memory. We also show neighborhood and model-based predictions can be combined to achieve further performance improvements1.

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders.

Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.Previous work shows the efficacy of jointly scoring and selecting sentences with neural sequence generation models.It is, however, not well-understood if the gain is due to better encoding techniques or better redundancy reduction approaches.Similarly, the contribution of salience versus diversity components on the created summary is not studied well.Building on the state-of-the-art encoding methods for summarization, we present two adaptive learning models: AREDSUM-SEQ that jointly considers salience and novelty during sentence selection; and a two-step AREDSUM-CTX that scores salience first, then learns to balance salience and redundancy, enabling the measurement of the impact of each aspect.Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling diversity explicitly in a separate step, AREDSUM-CTX achieves significantly better performance than AREDSUM-SEQ as well as state-of-the-art extractive summarization baselines.

Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

We derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.

Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.

Task oriented language understanding (LU) in human-to-machine (H2M) conversations has been extensively studied for personal digital assistants.In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task.Recent advances on LU in H2M conversations have shown accuracy improvements by adding encoded knowledge from different sources.Inspired by this, we explore several variants of a bidirectional LSTM architecture that relies on different knowledge sources, such as Web data, search engine click logs, expert feedback from H2M models, as well as previous utterances in the conversation.We also propose ensemble techniques that aggregate these different knowledge sources into a single model.Experimental evaluation on a four-turn Twitter dataset in the restaurant and music domains shows improvements in the slot tagging F1-score of up to 6.09% compared to existing approaches.

Abstract This paper summarizes ongoing research in Natural-Language-Processing-driven citation analysis and describes experiments and motivating examples of how this work can be used to enhance traditional scientometrics analysis that is based on simply treating citations as a ‘vote’ from the citing paper to cited paper. In particular, we describe our dataset for citation polarity and citation purpose, present experimental results on the automatic detection of these indicators, and demonstrate the use of such annotations for studying research dynamics and scientific summarization. We also look at two complementary problems that show up in Natural-Language-Processing-driven citation analysis for a specific target paper. The first problem is extracting citation context, the implicit citation sentences that do not contain explicit anchors to the target paper. The second problem is extracting reference scope, the target relevant segment of a complicated citing sentence that cites multiple papers. We show how these tasks can be helpful in improving sentiment analysis and citation-based summarization.

New scientific concepts, interpreted broadly, are continuously introduced in the literature, but relatively few concepts have a long‐term impact on society. The identification of such concepts is a challenging prediction task that would help multiple parties—including researchers and the general public—focus their attention within the vast scientific literature. In this paper we present a system that predicts the future impact of a scientific concept, represented as a technical term, based on the information available from recently published research articles. We analyze the usefulness of rich features derived from the full text of the articles through a variety of approaches, including rhetorical sentence analysis, information extraction, and time‐series analysis. The results from two large‐scale experiments with 3.8 million full‐text articles and 48 million metadata records support the conclusion that full‐text features are significantly more useful for prediction than metadata‐only features and that the most accurate predictions result from combining the metadata and full‐text features. Surprisingly, these results hold even when the metadata features are available for a much larger number of documents than are available for the full‐text features.

We investigate the task of generating coherent survey articles for scientific topics. We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic. Human evaluation on 15 topics in computational linguistics shows that our system produces significantly more coherent summaries than previous systems. Specifically, our system improves the ratings for coherence by 36% in human evaluation compared to C-Lexrank, a state of the art system for scientific article summarization.

Rahul Jha, Catherine Finegan-Dollak, Ben King, Reed Coke, Dragomir Radev. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

We present heterogeneous networks as a way to unify lexical networks with relational data. We build a unified ACL Anthology network, tying together the citation, author collaboration, and term-cooccurence networks with affiliation and venue relations. This representation proves to be convenient and allows problems such as name disambiguation, topic modeling, and the measurement of scientific impact to be easily solved using only this network and off-the-shelf graph algorithms.

One of the greatest administrative and security challenges within every IT and HR organization is provisioning — providing users with appropriate access to enterprise information and technology resources. This is especially true in today's volatile business environment, where employee turnover, cost cutting and consolidation are all constant occurrences. Traditionally, the provisioning process has been paper-based, processintensive, bureaucratic and hard to track. Today, it is even harder to manage because of the growing variety of systems within an organization and the high cost of managing user identities and permissions across organizations. Automated provisioning streamlines this process, giving new employees, partners and clients faster access to the resources they need to interact productively with your organization. A provisioning system also facilitates de-provisioning — the rapid removal of access rights when a user leaves the company — which is especially critical to enterprise security. Persistent’s enQuire Virtual Directory Server [3] is a product which provides a LDAP v3 standards based solution for automatic provisioning of user identities. It provides a unified view of a user’s network identity, which is typically fragmented over different systems in an organization. It has the capabilities to merge the different fragments of a user’s identity and provide the administrator of the provisioning system with a single consolidated identity for a user. To maintain the integrity and security constraints associated with such provisioning operations enQuire needs to provide transaction capabilities over the unified identity of a user. The network identity of a user is created, by linking together various fragments based on some common information, which is shared across the different identity fragments. So, whenever parts of this shared identity are updated it is imperative that consistency is maintained across the different systems from where the unified entry is created. This brings about the need of transactional support in performing these kinds of identity operations. enQuire internally uses LDAP to perform its identity management operations. Unfortunately, LDAP supports only atomic updates at the individual entry level and, today, the applications are forced to take ad-hoc measures to maintain consistency across multiple data repositories. A systematically developed transaction support for consistent updates across multiple data repositories is therefore imperative. The provisioning applications increasingly involve long duration activities and hence the traditional OLTP transaction support is inadequate. Thus the transaction support should support advanced transaction models, as well; or, at least, should have primitives to facilitate implementation of the advanced model with little effort. The users of the system expect the transaction support for LDAP to be as simple to use, as are LDAP primitives. With these compelling motivations, we have developed enTrans, a highly flexible and customizable transaction support facility that works with the enQuire Virtual Directory server. The enTrans framework provides advanced transaction support for provisioning applications and a mechanism to define and enforce application specific integrity constraints. enTrans uses Predefined Trigger Access Protocol (PTAP) [1] for providing the necessary transactional support.