pwc-archive/papers-with-abstracts huggingface dataset:
Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well
Authors: Christian Meilicke, Patrick Betz, Heiner Stuckenschmidt
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Knowledge Base Completion, Knowledge Graph Completion

We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essential differences between symbolic and latent approaches. Based on our insights, we construct a simple method to combine the outcome of rule-based and latent approaches in a post-processing step. Our method improves the results constantly for each model and dataset used in our experiments.
Enterprise Alexandria: Online High-Precision Enterprise Knowledge Base Construction with Typed Entities
Authors: John Winn, Matteo Venanzi, Tom Minka, Ivan Korostelev, John Guiver, Elena Pochernina, Pavel Mishkov, Alex Spengler, Denise Wilkins, Sian Lindley, Richard Banks, Sam Webster, Yordan Zaykov
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Knowledge Base Construction

We present Enterprise Alexandria, one of the core AI technologies behind Microsoft Viva Topics. Enterprise Alexandria is a new system for automatically constructing a knowledge base with high-precision and typed entities from private enterprise data such as emails, documents and intranet pages. Built as an extension of Alexandria [Winn et al.,2019], the key novelty of Enterprise Alexandria is the ability in processing both the textual information and the structured metadata available in each document in an online learning fashion, making use of any manual curations that have happened in the interim. This task is performed entirely eyes-off to respect the privacy of the user and the restricted access their documents. The knowledge discovery process uses a probabilistic program defining the process of generating the data item from a set of unknown typed entities. Using probabilistic inference, Enterprise Alexandria can jointly discover a large set of entities with custom types specific to the organization. Experiments on three real-world datasets show that the system outperforms alternative methods with the ability to work effectively at large scale.
Open Temporal Relation Extraction for Question Answering
Authors: Chao Shang, Peng Qi, Guangtao Wang, Jing Huang, Youzheng Wu, BoWen Zhou
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Question Answering, Reading Comprehension, Relation, Relation Extraction, Temporal Relation Extraction

Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA). When explicit timestamps are absent, TQA is a challenging task that requires models to understand the nuanced difference in textual expressions that indicate different temporal relations (e.g., "What happened right before dawn" indicates a small subset of "What happened before dawn"). In this paper, we propose to reformulate the task of TQA as open temporal relation extraction. Specifically, we decompose each question into a question event (e.g., "dawn") and an open temporal relation (OTR, e.g., "happened before") which is not pre-defined nor with timestamps, and ground the former in the context while sharing the representation of the latter across contexts. This OTR for QA formulation has two advantages: 1) it allows us to learn context-agnostic, free-text-based relation representations that generalize across different contexts and events, which leads to higher data efficiency; 2) it allows us to explicitly model the differences in temporal relations with a contrastive loss function, which helps better capture mutually exclusive relations (e.g., an event cannot simultaneously "happen before" and "happen after" another) as well as more nuanced differences (e.g., not everything that "happened before" an event "happened right before" it). Empirical evaluations on the TORQUE challenge, a recently released dataset for temporal ordering questions, show that our approach attains significant improvements correspondingly over the state of the art performance, especially gains more on EM consistency computed on the contrast question sets.
SHINRA2020-ML: Categorizing 30-language Wikipedia into fine-grained NE based on "``Resource by Collaborative Contribution" scheme
Authors: Satoshi Sekine, Kouta Nakayama, Maya Ando, Yu Usami, Masako Nomoto, Koji Matsuda
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Ensemble Learning, Knowledge Base Construction

This paper describes a Knowledge Base construction project, SHINRA and particularly the SHINRA2020-ML task. The SHINRA2020-ML task is to categorize 30-language Wikipedia pages into fine-grained named entity categories, called "Extended Named Entity (ENE)". It is one of the three tasks conducted on SHINRA since 2018. SHINRA is a collaborative contribution scheme that utilize Automatic Knowledge Base Construction (AKBC) systems. This project aims to create a huge and well-structured knowledge base essential for many NLP applications, such as QA, dialogue systems and explainable NLP systems.

In our "Resource by Collaborative Contribution (RbCC)" scheme, we conducted a shared task of structuring Wikipedia to attract participants but simultaneously submitted results are used to construct a knowledge base. One trick is that the participants are not notified of the test data, so they have to run their systems on all entities in Wikipedia, although the evaluation results are reported for only a small portion of the test data among the entire data. Using this method, the organizers receive multiple outputs of the entire data from the participants. The submitted outputs are publicly accessible and are applied to building better structured knowledge using ensemble learning, for example. In other words, this project uses AKBC systems to construct a huge and well-structured Knowledge Base collaboratively.

The "SHINRA2020-ML" task is also based on the RbCC scheme. The task categorizes 30-language Wikipedia pages into ENE. We previously categorized the entire Japanese Wikipedia entities (920 thousand entities) into ENE by ML and then checked by hands. For SHINRA2020-ML task participants, we provided the training data using categories of the Japanese Wikipedia and language links from the page. For example, out of 920K Japanese Wikipedia pages, 275K have language links to German pages. These data are
used to create the training data for German and the task is to categorize the remaining 1,946K pages. We conducted a shared task for 30 languages with the largest active users, and 10 groups participated. We showed that the results by simple ensemble learning, i.e.,
majority voting, exceed top results in 17 languages, thereby proving the usefulness of the "RbCC" scheme.

We are conducting two tasks in 2021, SHINRA2021-LinkJP and SHINRA2021-ML tasks. We will introduce these tasks in a later section of the paper.
One-shot to Weakly-Supervised Relation Classification using Language Models
Authors: Thy Thy Tran, Phong Le, Sophia Ananiadou
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Relation, Relation Classification

Relation classification aims at detecting a particular relation type between two entities in text, whose methods mostly requires annotated data. Data annotation is either a manual process for supervised learning, or automated, using knowledge bases for distant learning. Unfortunately, both annotation methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation. With recent evidence that language models capture some sort of relational facts as knowledge bases, one-shot relation classification using language models has been proposed via matching a given instance against examples. The only requirement is that each relation type is associated with an exemplar. However, the matching approach often yields incorrect predictions. In this work, we propose NoelA, an auto-encoder using a noisy channel, to improve the accuracy by learning from the matching predictions. NoelA outperforms BERT matching and a bootstrapping baseline on TACRED and reWiki80.
The Vagueness of Vagueness in Noun Phrases
Authors: Pierre-Henri Paris, Syrine El Aoud, Fabian M. Suchanek
Date: 2021-06-22 00:00:00
Proceeding: AKBC 2021 10
Tasks: Position

Natural language text has a great potential to feed knowledge bases. However, natural language is not always precise -- and sometimes intentionally so. In this position paper, we study vagueness in noun phrases. We manually analyze the frequency of vague noun phrases in a Wikipedia corpus, and find that 1/4 of noun phrases exhibit some form of vagueness. We report on their nature and propose a categorization. We then conduct a literature review and present different definitions of vagueness, and different existing methods to deal with the detection and modeling of vagueness. We find that, despite its frequency, vagueness has not yet be addressed in its entirety.
Improving Quality Assessment of Online Reviews UsingFormal Argumentation Theory and Knowledge Graphs
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10
Tasks: BIG-bench Machine Learning, Knowledge Graphs

In this extended abstract, we introduce a previously developed framework for the assessment of the quality of product reviews by using formal argumentation theory and machine learning.

Then, we outline current development on the use of knowledge graphs to better mine arguments, refine argumentation reasoning, and produce more meaningful explanations.
Annotating Implicit Reasoning in Arguments with Causal Links
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10

Most of the existing work that focus on the identification of implicit knowledge in arguments generally represent implicit knowledge in the form of commonsense or factual knowledge.
However, such knowledge is not sufficient to understand the implicit reasoning link between individual argumentative components (i.e., claim and premise).
In this work, we focus on identifying the implicit knowledge in the form of argumentation knowledge which can help in understanding the reasoning link in arguments.
Being inspired by the Argument from Consequences scheme, we propose a semi-structured template to represent such argumentation knowledge that explicates the implicit reasoning in arguments via causality.
We create a novel two-phase annotation process with simplified guidelines and show how to collect and filter high quality implicit reasonings via crowdsourcing.
We find substantial inter-annotator agreement for quality evaluation between experts, but find evidence that casts a few questions on the feasibility of collecting high quality semi-structured implicit reasoning through our crowdsourcing process.
We release our materials (i.e., crowdsourcing guidelines and collected implicit reasonings) to facilitate further research towards the structured representation of argumentation knowledge.
ArguMeet: An Argument Diagramming Schema for Meeting Conversations
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10

We propose a schema called ArguMeet for argument diagramming of multi-party meeting conversations. The argumentative portions are extracted from the meeting transcript and a visual representation is created which provides a high-level view of the argumentation that has taken place during the meeting resulting in a set of possible action points.
First Workshop on Argumentation Knowledge Graphs
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10
Tasks: Knowledge Graphs

First Workshop on Argumentation Knowledge Graphs
My research relevant to the Argument Knowledge Graph workshop
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10
Tasks: Articles

Some of my past and current research has addressed the generation and mining of scientific argumentation in monological text in the domain of genetic counseling and genetics research articles.  Rather than representing argumentation in a knowledge graph (KG), arguments represented in propositional logic were generated from knowledge bases (KBs) using argumentation schemes.  Argumentation schemes are abstract patterns describing types of arguments that are acceptable to a community.
End-to-end argumentation knowledge graph construction
Authors: Anonymous
Date: 2021-09-13 00:00:00
Proceeding: AKBC Workshop ArgKG 2021 10
Tasks: 16k, Fake News Detection, graph construction, Knowledge Graphs, Question Answering, Text Generation

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.
Story Generation with Commonsense Knowledge Graphs and Axioms
Authors: Filip Ilievski, Jay Pujara, Hanzhi Zhang
Date: 2021-09-03 00:00:00
Proceeding: AKBC Workshop CSKB 2021 10
Tasks: Common Sense Reasoning, Knowledge Graphs, Story Generation

Humans can understand stories, and the rich interactions between agents, locations, and events, seamlessly. However, state-of-the-art reasoning models struggle with understanding, completing, or explaining stories, often due to the complexity of the underlying common sense necessary for comprehension. One potential reason models perform poorly is the lack of large-scale training data that supplies annotations of the common sense necessary for story understanding. In this paper, we investigate the generation of stories at scale, by combining commonsense axioms with commonsense knowledge graphs to produce stories annotated with common sense. We first demonstrate that commonsense axioms and commonsense knowledge graphs are sufficient to capture the underlying narratives in a popular story corpus. Our method aligns story types with commonsense axioms, and queries to a commonsense knowledge graph, enabling the generation of  hundreds of thousands of stories. We evaluate these stories for sensibility and interestingness through a crowdsourcing task. Using our story corpus, we also design a probing task with questions for three exemplar story types. Our results show that our method generates story endings of a higher quality compared to the current generative language models. This work points to key open challenges around generating better stories, providing more comprehensive explanations, and building models that can explain any story with axioms.
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models
Authors: Rachit Bansal, Milan Aggarwal, Sumit Bhatia, Jivat Neet Kaur, Balaji Krishnamurthy
Date: 2021-09-03 00:00:00
Proceeding: AKBC Workshop CSKB 2021 10
Tasks: ARC, Knowledge Graphs, Novel Concepts, Sentence

Pre-trained Language Models (PTLMs) have been shown to perform well on natural language reasoning tasks requiring commonsense. Prior work has leveraged structured commonsense present in knowledge graphs (KGs) to assist PTLMs. Some of these methods use KGs as separate static modules which limits knowledge coverage since KGs are finite, sparse, and noisy. Other methods have attempted to obtain generalized and scalable commonsense by training PTLMs on KGs. Since they are trained on symbolic KG phrases, applying them on natural language text during inference leads to input distribution shift. To this end, we propose a task agnostic sentence-conditioned generative CommonSense Contextualizer (CoSe-Co), which is trained to generate contextually relevant commonsense inferences given a natural language input. We devise a method to create semantically related sentence-commonsense pairs to train CoSe-Co. We observe commonsense inferences generated by CoSe-Co contain novel concepts that are relevant to the entire sentence context. We evaluate CoSe-Co on multi-choice QA and open-ended commonsense reasoning tasks on the CSQA, ARC, QASC, and OBQA datasets. CoSe-Co outperforms state-of-the-art methods in both these settings, while being task-agnostic, and performs especially well in low data regimes showing it is more robust and generalises better.
No Need to Know Everything! Efficiently Augmenting Language Models With External Knowledge
Authors: Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal, Rachit Bansal, Balaji Krishnamurthy
Date: 2021-09-03 00:00:00
Proceeding: AKBC Workshop CSKB 2021 10
Tasks: Language Modeling, Language Modelling

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture semantic, syntactic, and factual knowledge in their parameters. However, storing large amounts of factual knowledge in the parameters of the model is sub-optimal given the resource requirements and ever-growing amounts of knowledge. Instead of packing all the knowledge in the model parameters, we argue that a more efficient alternative is to provide contextually relevant structured knowledge to the model and train it to use that knowledge. This allows the training of the language model to be de-coupled from the external knowledge source and the latter can be updated without affecting the parameters of the language model. Empirical evaluation using different subsets of LAMA probe reveals that such an approach allows smaller language models with access to external knowledge to achieve significant and robust outperformance over much larger language models.
Cloud Database Tuning with Reinforcement Learning
Authors: Chenxia Han, Chaokun Chang
Date: 2020-12-14 00:00:00
Proceeding: CUHK Course IERG5350 2020 12
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In database management systems (DBMSs), especially cloud DBMSs, configuration tuning is one of the key factors that influence database performance. For a long period of time, the tuning job of databases is done by experienced database administrators (DBAs), which is time-consuming and sub-optimal. Recently, with the development of machine learning, automatic tuning tool starts to play a significant role in DBMSs. Among all the learning-based methods, reinforcement learning has the greatest potentiality to find the optimal or near-optimal configuration. There are already some reinforcement learning applications in database tuning, nonetheless, none of them provide fully executable code. In this project, we implement an independent version of auto-tuner to re-produce the current works on MySQL. Based on a careful design of the system, our code can be easily applied to other databases like JDB, LevelDB, etc. We believe it will benefit a lot to those database administrators who know a lot about databases but lack the knowledge of reinforcement learning. The code is available at https://github.com/ChaokunChang/GDBTuner. The 5 minutes video can be found at Google Drive https://drive.google.com/drive/folders/1Z1MvtBnQ522zPElNu-VSrAMvKcX523LE?usp=sharing.
Reinforcement Learning for the Beginning of Starcraft II Game
Authors: Yukang Chen, Ruihang Chu
Date: 2020-12-14 00:00:00
Proceeding: CUHK Course IERG5350 2020 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Starcraft II is a popular real-time strategy game that is welcomed by many young people. This game is really complicated to play due to the long game timeline, various society/buildings/units, a large number of actions or selections, different constraints (e.g., population limit), and the partially observed environments. In this project, we plan to develop a reinforcement learning model for the beginning of Starcraft II game, instead of the full-length game. The beginning of the game is essential for the further economy, population increase, and technology development. Our project is based on the SC2LE (StarCraft II Learning Environment) platform. We build a feasible pipeline for training reinforcement learning models and design random, scripted, and our actor-critic based agents. Experiments show that our actor-critic based agents can learn valuable knowledge in this task. The video is available at https://drive.google.com/file/d/11S6t3rNKjM1CJEkFSN1lkZiyLTdqHhzQ/view?usp=sharing
Evading Web Application Firewalls with Reinforcement Learning
Authors: Xianbo Wang, Han Hu
Date: 2020-12-14 00:00:00
Proceeding: CUHK Course IERG5350 2020 12
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Web Application Firewalls (WAF) are widely deployed to protect web servers from security threats like SQL injections. 
WAF products employ various techniques, e.g., syntax signature and machine learning, to detect and block suspicious web traffics. 
However, no WAF can be absolutely secure, there are always space for adversaries to craft malicious messages that can evade the detection.
In the past, most evasion techniques are developed manually, which requires labour and intelligence. 
In this work, we propose to explore the possibility of automating the process of WAF evasion using reinforcement learning. We created a reinforcement learning environment (based on OpenAI gym) for WAF evasion tasks and evaluate various mainstream WAF products with Proximal Policy Optimization (PPO) algorithm. Our framework successfully discovered numbers of evasion payloads for each WAF in our experiments and can significantly outperform baseline policy. Finally, we extract common patterns from the discovered evasion payloads and discuss weaknesses/flaws of existing WAF products as well as suggested improvements. 

(Our 5-minutes video presentation can be accessed at https://bit.ly/3gGwfBa with password DE@DB33F)
Portfolio Management with Reinforcement Learning
Authors: Zikai Sun, YUTING AN
Date: 2020-12-14 00:00:00
Proceeding: CUHK Course IERG5350 2020 12
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Portfolio management is a crucial trading task for investment companies in the market. In this work, reinforcement learning (RL) incorporating the transformer structure is combined with deep learning (DL) to build an automated portfolio management model. The proposed method uses the Sharpe ratio along with transaction cost as the reward and build an environment that contains the whole A-share market to train the RL agent. The result demonstrates that the trained strategy outperforms 
The Shanghai Composite Index.