Publications - The Year Before Last

Commonsense knowledge about everyday concepts is an important asset for AI
applications, such as question answering and chatbots. Recently, we have seen
an increasing interest in the construction of structured commonsense knowledge
bases (CSKBs). An important part of human commonsense is about properties that
do not apply to concepts, yet existing CSKBs only store positive statements.
Moreover, since CSKBs operate under the open-world assumption, absent
statements are considered to have unknown truth rather than being invalid. This
paper presents the UNCOMMONSENSE framework for materializing informative
negative commonsense statements. Given a target concept, comparable concepts
are identified in the CSKB, for which a local closed-world assumption is
postulated. This way, positive statements about comparable concepts that are
absent for the target concept become seeds for negative statement candidates.
The large set of candidates is then scrutinized, pruned and ranked by
informativeness. Intrinsic and extrinsic evaluations show that our method
significantly outperforms the state-of-the-art. A large dataset of informative
negations is released as a resource for future research.

Commonsense knowledge about everyday concepts is an important asset for AI
applications, such as question answering and chatbots. Recently, we have seen
an increasing interest in the construction of structured commonsense knowledge
bases (CSKBs). An important part of human commonsense is about properties that
do not apply to concepts, yet existing CSKBs only store positive statements.
Moreover, since CSKBs operate under the open-world assumption, absent
statements are considered to have unknown truth rather than being invalid. This
paper presents the UNCOMMONSENSE framework for materializing informative
negative commonsense statements. Given a target concept, comparable concepts
are identified in the CSKB, for which a local closed-world assumption is
postulated. This way, positive statements about comparable concepts that are
absent for the target concept become seeds for negative statement candidates.
The large set of candidates is then scrutinized, pruned and ranked by
informativeness. Intrinsic and extrinsic evaluations show that our method
significantly outperforms the state-of-the-art. A large dataset of informative
negations is released as a resource for future research.

Conversational question answering (ConvQA) tackles sequential information
needs where contexts in follow-up questions are left implicit. Current ConvQA
systems operate over homogeneous sources of information: either a knowledge
base (KB), or a text corpus, or a collection of tables. This paper addresses
the novel issue of jointly tapping into all of these together, this way
boosting answer coverage and confidence. We present CONVINSE, an end-to-end
pipeline for ConvQA over heterogeneous sources, operating in three stages: i)
learning an explicit structured representation of an incoming question and its
conversational context, ii) harnessing this frame-like representation to
uniformly capture relevant evidences from KB, text, and tables, and iii)
running a fusion-in-decoder model to generate the answer. We construct and
release the first benchmark, ConvMix, for ConvQA over heterogeneous sources,
comprising 3000 real-user conversations with 16000 questions, along with entity
annotations, completed question utterances, and question paraphrases.
Experiments demonstrate the viability and advantages of our method, compared to
state-of-the-art baselines.

Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge canonicalization and so on. By constructing structured knowledge from natural language text, knowledge extraction becomes a key asset for search engines, question answering and other downstream applications. However, current knowledge extraction methods mostly focus on prominent real-world entities with Wikipedia and mainstream news articles as sources. The constructed knowledge bases, therefore, lack information about long-tail domains, with fiction and fantasy as archetypes. Fiction and fantasy are core parts of our human culture, spanning from literature to movies, TV series, comics and video games. With thousands of fictional universes which have been created, knowledge from fictional domains are subject of search-engine queries - by fans as well as cultural analysts. Unlike the real-world domain, knowledge extraction on such specific domains like fiction and fantasy has to tackle several key challenges: - Training data: Sources for fictional domains mostly come from books and fan-built content, which is sparse and noisy, and contains difficult structures of texts, such as dialogues and quotes. Training data for key tasks such as taxonomy induction, named entity typing or relation extraction are also not available. - Domain characteristics and diversity: Fictional universes can be highly sophisticated, containing entities, social structures and sometimes languages that are completely different from the real world. State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains. With different genres of fictional domains, another requirement is to transfer models across domains. - Long fictional texts: While state-of-the-art models have limitations on the input sequence length, it is essential to develop methods that are able to deal with very long texts (e.g. entire books), to capture multiple contexts and leverage widely spread cues. This dissertation addresses the above challenges, by developing new methodologies that advance the state of the art on knowledge extraction in fictional domains. - The first contribution is a method, called TiFi, for constructing type systems (taxonomy induction) for fictional domains. By tapping noisy fan-built content from online communities such as Wikia, TiFi induces taxonomies through three main steps: category cleaning, edge cleaning and top-level construction. Exploiting a variety of features from the original input, TiFi is able to construct taxonomies for a diverse range of fictional domains with high precision. - The second contribution is a comprehensive approach, called ENTYFI, for named entity recognition and typing in long fictional texts. Built on 205 automatically induced high-quality type systems for popular fictional domains, ENTYFI exploits the overlap and reuse of these fictional domains on unseen texts. By combining different typing modules with a consolidation stage, ENTYFI is able to do fine-grained entity typing in long fictional texts with high precision and recall. - The third contribution is an end-to-end system, called KnowFi, for extracting relations between entities in very long texts such as entire books. KnowFi leverages background knowledge from 142 popular fictional domains to identify interesting relations and to collect distant training samples. KnowFi devises a similarity-based ranking technique to reduce false positives in training samples and to select potential text passages that contain seed pairs of entities. By training a hierarchical neural network for all relations, KnowFi is able to infer relations between entity pairs across long fictional texts, and achieves gains over the best prior methods for relation extraction.

In this thesis we explore pattern mining and deep learning. Often seen as orthogonal, we show that these fields complement each other and propose to combine them to gain from each other’s strengths. We, first, show how to efficiently discover succinct and non-redundant sets of patterns that provide insight into data beyond conjunctive statements. We leverage the interpretability of such patterns to unveil how and which information flows through neural networks, as well as what characterizes their decisions. Conversely, we show how to combine continuous optimization with pattern discovery, proposing a neural network that directly encodes discrete patterns, which allows us to apply pattern mining at a scale orders of magnitude larger than previously possible. Large neural networks are, however, exceedingly expensive to train for which ‘lottery tickets’ – small, well-trainable sub-networks in randomly initialized neural networks – offer a remedy. We identify theoretical limitations of strong tickets and overcome them by equipping these tickets with the property of universal approximation. To analyze whether limitations in ticket sparsity are algorithmic or fundamental, we propose a framework to plant and hide lottery tickets. With novel ticket benchmarks we then conclude that the limitation is likely algorithmic, encouraging further developments for which our framework offers means to measure progress.

In this work we address the challenging case of answering count queries in
web search, such as ``number of songs by John Lennon''. Prior methods merely
answer these with a single, and sometimes puzzling number or return a ranked
list of text snippets with different numbers. This paper proposes a methodology
for answering count queries with inference, contextualization and explanatory
evidence. Unlike previous systems, our method infers final answers from
multiple observations, supports semantic qualifiers for the counts, and
provides evidence by enumerating representative instances. Experiments with a
wide variety of queries, including existing benchmark show the benefits of our
method, and the influence of specific parameter settings. Our code, data and an
interactive system demonstration are publicly available at
github.com/ghoshs/CoQEx and nlcounqer.mpi-inf.mpg.de.

Social media communities like Reddit and Twitter allow users to express their views on
topics of their interest, and to engage with other users who may share or oppose these views.
This can lead to productive discussions towards a consensus, or to contended debates, where
disagreements frequently arise.
Prior work on such settings has primarily focused on identifying notable instances of antisocial
behavior such as hate-speech and “trolling”, which represent possible threats to the health of
a community. These, however, are exceptionally severe phenomena, and do not encompass
controversies stemming from user debates, differences of opinions, and off-topic content, all
of which can naturally come up in a discussion without going so far as to compromise its
development.
This dissertation proposes a framework for the systematic analysis of social media discussions
that take place in the presence of controversial themes, disagreements, and mixed opinions from
participating users. For this, we develop a feature-based model to describe key elements of a
discussion, such as its salient topics, the level of activity from users, the sentiments it expresses,
and the user feedback it receives.
Initially, we build our feature model to characterize adversarial discussions surrounding
political campaigns on Twitter, with a focus on the factual and sentimental nature of their
topics and the role played by different users involved. We then extend our approach to Reddit
discussions, leveraging community feedback signals to define a new notion of controversy
and to highlight conversational archetypes that arise from frequent and interesting interaction
patterns. We use our feature model to build logistic regression classifiers that can predict future
instances of controversy in Reddit communities centered on politics, world news, sports, and
personal relationships. Finally, our model also provides the basis for a comparison of different
communities in the health domain, where topics and activity vary considerably despite their
shared overall focus. In each of these cases, our framework provides insight into how user
behavior can shape a community’s individual definition of controversy and its overall identity.

As machine learning (ML) is increasingly used for decision making in scenarios that impact humans, there is a growing awareness of its potential for unfairness. A large body of recent work has focused on proposing formal notions of fairness in ML, as well as approaches to mitigate unfairness. However, there is a growing disconnect between the ML fairness literature and the needs to operationalize fairness in practice. This thesis addresses the need for responsible ML by developing new models and methods to address challenges in operationalizing fairness in practice. Specifically, it makes the following contributions. First, we tackle a key assumption in the group fairness literature that sensitive demographic attributes such as race and gender are known upfront, and can be readily used in model training to mitigate unfairness. In practice, factors like privacy and regulation often prohibit ML models from collecting or using protected attributes in decision making. To address this challenge we introduce the novel notion of computationally-identifiable errors and propose Adversarially Reweighted Learning (ARL), an optimization method that seeks to improve the worst-case performance over unobserved groups, without requiring access to the protected attributes in the dataset. Second, we argue that while group fairness notions are a desirable fairness criterion, they are fundamentally limited as they reduce fairness to an average statistic over pre-identified protected groups. In practice, automated decisions are made at an individual level, and can adversely impact individual people irrespective of the group statistic. We advance the paradigm of individual fairness by proposing iFair (individually fair representations), an optimization approach for learning a low dimensional latent representation of the data with two goals: to encode the data as well as possible, while removing any information about protected attributes in the transformed representation. Third, we advance the individual fairness paradigm, which requires that similar individuals receive similar outcomes. However, similarity metrics computed over observed feature space can be brittle, and inherently limited in their ability to accurately capture similarity between individuals. To address this, we introduce a novel notion of fairness graphs, wherein pairs of individuals can be identified as deemed similar with respect to the ML objective. We cast the problem of individual fairness into graph embedding, and propose PFR (pairwise fair representations), a method to learn a unified pairwise fair representation of the data. Fourth, we tackle the challenge that production data after model deployment is constantly evolving. As a consequence, in spite of the best efforts in training a fair model, ML systems can be prone to failure risks due to a variety of unforeseen reasons. To ensure responsible model deployment, potential failure risks need to be predicted, and mitigation actions need to be devised, for example, deferring to a human expert when uncertain or collecting additional data to address model’s blind-spots. We propose Risk Advisor, a model-agnostic meta-learner to predict potential failure risks and to give guidance on the sources of uncertainty inducing the risks, by leveraging information theoretic notions of aleatoric and epistemic uncertainty. This dissertation brings ML fairness closer to real-world applications by developing methods that address key practical challenges. Extensive experiments on a variety of real-world and synthetic datasets show that our proposed methods are viable in practice.

Automated methods have been widely used to identify and analyze mental health
conditions (e.g., depression) from various sources of information, including
social media. Yet, deployment of such models in real-world healthcare
applications faces challenges including poor out-of-domain generalization and
lack of trust in black box models. In this work, we propose approaches for
depression detection that are constrained to different degrees by the presence
of symptoms described in PHQ9, a questionnaire used by clinicians in the
depression screening process. In dataset-transfer experiments on three social
media datasets, we find that grounding the model in PHQ9's symptoms
substantially improves its ability to generalize to out-of-distribution data
compared to a standard BERT-based approach. Furthermore, this approach can
still perform competitively on in-domain data. These results and our
qualitative analyses suggest that grounding model predictions in
clinically-relevant symptoms can improve generalizability while producing a
model that is easier to inspect.

Structured knowledge is important for many AI applications. Commonsense
knowledge, which is crucial for robust human-centric AI, is covered by a small
number of structured knowledge projects. However, they lack knowledge about
human traits and behaviors conditioned on socio-cultural contexts, which is
crucial for situative AI. This paper presents CANDLE, an end-to-end methodology
for extracting high-quality cultural commonsense knowledge (CCSK) at scale.
CANDLE extracts CCSK assertions from a huge web corpus and organizes them into
coherent clusters, for 3 domains of subjects (geography, religion, occupation)
and several cultural facets (food, drinks, clothing, traditions, rituals,
behaviors). CANDLE includes judicious techniques for classification-based
filtering and scoring of interestingness. Experimental evaluations show the
superiority of the CANDLE CCSK collection over prior works, and an extrinsic
use case demonstrates the benefits of CCSK for the GPT-3 language model. Code
and data can be accessed at cultural-csk.herokuapp.com.

Compiling comprehensive repositories of commonsense knowledge is a
long-standing problem in AI. Many concerns revolve around the issue of
reporting bias, i.e., that frequency in text sources is not a good proxy for
relevance or truth. This paper explores whether children's texts hold the key
to commonsense knowledge compilation, based on the hypothesis that such content
makes fewer assumptions on the reader's knowledge, and therefore spells out
commonsense more explicitly. An analysis with several corpora shows that
children's texts indeed contain much more, and more typical commonsense
assertions. Moreover, experiments show that this advantage can be leveraged in
popular language-model-based commonsense knowledge extraction settings, where
task-unspecific fine-tuning on small amounts of children texts (childBERT)
already yields significant improvements. This provides a refreshing perspective
different from the common trend of deriving progress from ever larger models
and corpora.

Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications. Background facts about users can allow chatbot assistants to produce more topical and empathic replies. In the context of recommendation and retrieval models, personal facts can be used to customize the ranking results for individual users. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge. Such knowledge bases are easily interpretable and can provide users with full control over their own personal knowledge, including revising stored facts and managing access by downstream services for personalization purposes. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Mainstream extraction methods specialize on well-structured data, such as biographical texts or encyclopedic articles, which are rare for most people. In turn, conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts. In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers’ personal attributes: • Demographic attributes, age, gender, profession and family status, are inferred by HAMs - hierarchical neural classifiers with attention mechanism. Trained HAMs can be transferred between different types of conversational data and provide interpretable predictions. • Long-tailed personal attributes, hobby and profession, are predicted with CHARM - a zero-shot learning model, overcoming the lack of labeled training samples for rare attribute values. By linking conversational utterances to external sources, CHARM is able to predict attribute values which it never saw during training. • Interpersonal relationships are inferred with PRIDE - a hierarchical transformer-based model. To accurately predict fine-grained relationships, PRIDE leverages personal traits of the speakers and the style of conversational utterances. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.

Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This thesis presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.