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Yonatan Bilu

Publications and source records attributed to Yonatan Bilu.

17 recordsLinked to original sources

Financial Event Extraction Using Wikipedia-Based Weak Supervision

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

cs.CL

The workweek is the best time to start a family -- A Study of GPT-2 Based Claim Generation

Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.

cs.CL

Multilingual Argument Mining: Datasets and Analysis

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.

cs.CL

What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus

One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia. In this work we ask what makes a term worthy of entering this edifice of knowledge, and having a page of its own in Wikipedia? To what extent is this a natural product of on-going human discourse and discussion rather than an idiosyncratic choice of Wikipedia editors? Specifically, we aim to identify such "wiki-worthy" terms in a massive news corpus, and see if this can be done with no, or minimal, dependency on actual Wikipedia entries. We suggest a five-step pipeline for doing so, providing baseline results for all five, and the relevant datasets for benchmarking them. Our work sheds new light on the domain-specific Automatic Term Extraction problem, with the problem at hand being a domain-independent variant of it.

cs.CL

Out of the Echo Chamber: Detecting Countering Debate Speeches

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns -- that of detecting articles that most effectively counter the arguments -- and not just the stance -- made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.

cs.CL

Corpus Wide Argument Mining -- a Working Solution

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.

cs.CL

A Dataset of General-Purpose Rebuttal

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

cs.CL

Argument Invention from First Principles

Competitive debaters often find themselves facing a challenging task -- how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on "first principles", commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a "first attempt" it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.

cs.CL

Controversy in Context

With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior works relied on Wikipedia's metadata and on content analysis of the articles pertaining to a concept in question. Here we show that the immediate textual context of a concept is strongly indicative of this property, and, using simple and language-independent machine-learning tools, we leverage this observation to achieve state-of-the-art results in controversiality prediction. In addition, we analyze and make available a new dataset of concepts labeled for controversiality. It is significantly larger than existing datasets, and grades concepts on a 0-10 scale, rather than treating controversiality as a binary label.

cs.CL

Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining

Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.

cs.CL

On the practically interesting instances of MAXCUT

The complexity of a computational problem is traditionally quantified based on the hardness of its worst case. This approach has many advantages and has led to a deep and beautiful theory. However, from the practical perspective, this leaves much to be desired. In application areas, practically interesting instances very often occupy just a tiny part of an algorithm's space of instances, and the vast majority of instances are simply irrelevant. Addressing these issues is a major challenge for theoretical computer science which may make theory more relevant to the practice of computer science. Following Bilu and Linial, we apply this perspective to MAXCUT, viewed as a clustering problem. Using a variety of techniques, we investigate practically interesting instances of this problem. Specifically, we show how to solve in polynomial time distinguished, metric, expanding and dense instances of MAXCUT under mild stability assumptions. In particular, $(1+ε)$-stability (which is optimal) suffices for metric and dense MAXCUT. We also show how to solve in polynomial time $Ω(\sqrt{n})$-stable instances of MAXCUT, substantially improving the best previously known result.

cs.CC

Are stable instances easy?

We introduce the notion of a stable instance for a discrete optimization problem, and argue that in many practical situations only sufficiently stable instances are of interest. The question then arises whether stable instances of NP--hard problems are easier to solve. In particular, whether there exist algorithms that solve correctly and in polynomial time all sufficiently stable instances of some NP--hard problem. The paper focuses on the Max--Cut problem, for which we show that this is indeed the case.

cs.CC

On the functional properties of microRNA-mediated feed forward loops

Motivation: Recent studies of genomic-scale regulatory networks suggested that a feed-forward loop (FFL) circuitry is a key component of many such networks. This led to a study of the functional properties of different FFL types, where the regulatory elements are transcription factors. Results: Here we investigate these properties when the mediating regulatory element of the loop is a microRNA. We find that many of the FFL properties are enhanced within this setup. We then proceed to identify all such FFLs in the D. Melanogaster regulatory network. We observe that in FFLs rooted at the same transcription factor there are significant correlations between the number of predicted binding sites for the transcription factor and for the microRNA. Conclusions: Based on a modeling approach we suggest that these correlations may be an outcome of the type of FFL preferred by the transcription factor. This may help elucidate the type of regulation the TF confers.

q-bio.MN

The evolution of microRNA-regulation in duplicated genes facilitates expression divergence

Background: The evolution of microRNA regulation in metazoans is a mysterious process: MicroRNA sequences are highly conserved among distal organisms, but on the other hand, there is no evident conservation of their targets. Results: We study this extensive rewiring of the microRNA regulatory network by analyzing the evolutionary trajectories of duplicated genes in D. melanogatser. We find that in general microRNA-targeted genes tend to avoid gene duplication. However, in cases where gene duplication is evident, we find that the gene that displays high divergence from the ancestral gene at the sequence level is also likely to be associated in an opposing manner with the microRNA regulatory system - if the ancestral gene is a miRNA target then the divergent gene tends not to be, and vice versa. Conclusions: This suggests that miRNAs not only have a role in conferring expression robustness, as was suggested by previous works, but are also an accessible tool in evolving expression divergence.

q-bio.MN

Tales of Hoffman

Hofmman's bound on the chromatic number of a graph states that $χ\geq 1 - \frac {λ_1} {λ_n}$. Here we show that the same bound, or slight modifications of it, hold for several graph parameters related to the chromatic number: the vector coloring number, the $ψ$-covering number and the $λ$-clustering number.

math.CO

Constructing expander graphs by 2-lifts and discrepancy vs. spectral gap

We present a new explicit construction for expander graphs with nearly optimal spectral gap. The construction is based on a series of 2-lift operations. Let $G$ be a graph on $n$ vertices. A 2-lift of $G$ is a graph $H$ on $2n$ vertices, with a covering map $π:H \to G$. It is not hard to see that all eigenvalues of $G$ are also eigenvalues of $H$. In addition, $H$ has $n$ ``new'' eigenvalues. We conjecture that every $d$-regular graph has a 2-lift such that all new eigenvalues are in the range $[-2\sqrt{d-1},2\sqrt{d-1}]$ (If true, this is tight, e.g. by the Alon-Boppana bound). Here we show that every graph of maximal degree $d$ has a 2-lift such that all ``new'' eigenvalues are in the range $[-c \sqrt{d \log^3d}, c \sqrt{d \log^3d}]$ for some constant $c$. This leads to a polynomial time algorithm for constructing arbitrarily large $d$-regular graphs, with second eigenvalue $O(\sqrt{d \log^3 d})$. The proof uses the following lemma: Let $A$ be a real symmetric matrix such that the $l_1$ norm of each row in $A$ is at most $d$. Let $α= \max_{x,y \in \{0,1\}^n, supp(x)\cap supp(y)=\emptyset} \frac {|xAy|} {||x||||y||}$. Then the spectral radius of $A$ is at most $c α\log(d/α)$, for some universal constant $c$. An interesting consequence of this lemma is a converse to the Expander Mixing Lemma.

math.CO

Monotone Maps, Sphericity and Bounded Second Eigenvalue

We consider {\em monotone} embeddings of a finite metric space into low dimensional normed space. That is, embeddings that respect the order among the distances in the original space. Our main interest is in embeddings into Euclidean spaces. We observe that any metric on $n$ points can be embedded into $l_2^n$, while, (in a sense to be made precise later), for almost every $n$-point metric space, every monotone map must be into a space of dimension $Ω(n)$. It becomes natural, then, to seek explicit constructions of metric spaces that cannot be monotonically embedded into spaces of sublinear dimension. To this end, we employ known results on {\em sphericity} of graphs, which suggest one example of such a metric space - that defined by a complete bipartitegraph. We prove that an $δn$-regular graph of order $n$, with bounded diameter has sphericity $Ω(n/(λ_2+1))$, where $λ_2$ is the second largest eigenvalue of the adjacency matrix of the graph, and $0 < δ\leq \half$ is constant. We also show that while random graphs have linear sphericity, there are {\em quasi-random} graphs of logarithmic sphericity. For the above bound to be linear, $λ_2$ must be constant. We show that if the second eigenvalue of an $n/2$-regular graph is bounded by a constant, then the graph is close to being complete bipartite. Namely, its adjacency matrix differs from that of a complete bipartite graph in only $o(n^2)$ entries. Furthermore, for any $0 < δ< \half$, and $λ_2$, there are only finitely many $δn$-regular graphs with second eigenvalue at most $λ_2$.

math.CO