Articles

Social spambots, an emerging class of spam- mers attempting to emulate people, are dif- ficult for both human annotators and classic bot detection techniques to reliably distinguish from genuine accounts. We examine this hu- man emulation through studying the human characteristics (personality, gender, age, emo- tions) exhibited by social spambots’ …...

Defeasible reasoning is a mode of reasoning where conclusions can be overturned by tak- ing into account new evidence. A commonly used method in cognitive science and logic lit- erature is to handcraft argumentation support- ing inference graphs. While humans find in- ference graphs very useful for reasoning, con- structing …...

Much of modern-day text simplification re- search focuses on sentence-level simplifica- tion, transforming original, more complex sen- tences into simplified versions. However, adding content can often be useful when dif- ficult concepts and reasoning need to be ex- plained. In this work, we present the first data- driven study of …...

ive Dialogue Summarization with Sketch Supervision Chien-Sheng Wu∗1, Linqing Liu∗2, Wenhao Liu1, Pontus Stenetorp2, Caiming Xiong1 1 Salesforce Research 2 University College London {linqing.liu,p.stenetorp}@cs.ucl.ac.uk, {wu.jason, wenhao.liu, cxiong}@salesforce.com Abstract In this paper, we aim to improve abstractive di- alogue summarization quality and, at the same time, enable granularity control. Our model …...

Quality Estimation (QE) is the task of automat- ically predicting Machine Translation quality in the absence of reference translations, mak- ing it applicable in real-time settings, such as translating online social media conversa- tions. Recent success in QE stems from the use of multilingual pre-trained representations, where very large models …...

Coreference resolution has been mostly in- vestigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained rela- tively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the …...

The growing ecosystem of data sharing in sci- ence has put dataset search into the focus. To make data sharing and reuse more feasible, new retrieval tools and services are being de- veloped. Currently, dataset retrieval relies al- most exclusively on metadata provided by the publishers. To extend this knowledge …...

Existing neural end-to-end dialogue models have limitations on exactly interpreting the lin- guistic structures, such as ellipsis, anaphor and co-reference, etc., in dialogue history context. Therefore, it is hard to determine whether the dialogue models truly understand a dialogue or not, only depending on the coherence evalu- ation of their …...

Large-scale conversational assistants like Alexa, Siri, Cortana and Google Assistant process every utterance using multiple models for domain, intent and named entity recognition. Given the decoupled nature of model development and large traffic volumes, it is extremely difficult to identify utterances processed erroneously by such systems. We address this challenge …...

End-to-End task-oriented dialogue systems generate responses based on dialog history and an accompanying knowledge base (KB). Infer- ring those KB entities that are most relevant for an utterance is crucial for response gener- ation. Existing state of the art scales to large KBs by softly filtering over irrelevant KB in- …...

Automatic speech recognition (ASR) in San- skrit is interesting, owing to the various lin- guistic peculiarities present in the language. The Sanskrit language is lexically productive, undergoes euphonic assimilation of phones at the word boundaries and exhibits variations in spelling conventions and in pronunciations. In this work, we propose the …...

Defining a sophisticated action space for a dia- log agent is essential for efficient training with reinforcement learning (RL). Recent work in- troduces discrete latent variables to use as an action space; however, a limitation is that a global vector can contain entangled informa- tion such as dialog act, sentence …...

To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most ex- isting works focus on rendering specified emo- tions in responses or empathetically respond to the emotion of users, yet the individual dif- ference in emotion expression …...

ive Multi-Document Summarization Shuaiqi LIU, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen Department of Computing, The Hong Kong Polytechnic University {cssqliu, csjcao, csryang, cszwen}@comp.polyu.edu.hk Abstract Abstractive multi-document summarization aims to generate a comprehensive summary covering salient content from multiple input documents. Compared with previous RNN- based models, the Transformer-based models …...

The high subjectivity and costs inherent in peer reviewing have recently motivated the preliminary design of machine learning-based acceptance decision methods. However, such approaches are limited in that they: a) do not explore the usage of both the reviewer and area chair recommendations, b) do not explicitly model subjectivity on …...

The attention-based encoder-decoder frame- work is widely used in many natural lan- guage generation tasks. The attention mecha- nism builds alignments between target words and source items that facilitate text genera- tion. Previous work proposes supervised at- tention that uses human knowledge to guide the attention mechanism to learn better …...

Radiology reports play a critical role in com- municating medical findings to physicians. In each report, the impression section summa- rizes essential radiology findings. In clini- cal practice, writing impression is highly de- manded yet time-consuming and prone to er- rors for radiologists. Therefore, automatic impression generation has emerged as …...

Cross-domain slot filling focuses on using la- beled data from source domains to train a slot filling model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing work focused on building a cross-domain transfer model. From the perspective of …...

We investigate the semantic knowledge of lan- guage models (LMs), focusing on (1) whether these LMs create categories of linguistic envi- ronments based on their semantic monotonic- ity properties, and (2) whether these categories play a similar role in LMs as in human lan- guage understanding, using negative polarity item …...

Transfer learning from pre-trained neural lan- guage models towards downstream tasks has been a predominant theme in NLP recently. Several researchers have shown that deep NLP models learn non-trivial amount of linguis- tic knowledge, captured at different layers of the model. We investigate how fine-tuning towards downstream NLP tasks impacts …...