Transfer Learning for Neural Semantic Parsing

The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning. We explore three multi-task architectures for sequence-to-sequence modeling and compare their performance with an independently trained model. Our experiments show that the multi-task setup aids transfer learning from an auxiliary task with large labeled data to a target task with smaller labeled data. We see absolute accuracy gains ranging from 1.0% to 4.4% in our in- house data set, and we also see good gains ranging from 2.5% to 7.0% on the ATIS semantic parsing tasks with syntactic and semantic auxiliary tasks.Comment: Accepted for ACL Repl4NLP 201

New developments in the theory of Groebner bases and applications to formal verification

We present foundational work on standard bases over rings and on Boolean Groebner bases in the framework of Boolean functions. The research was motivated by our collaboration with electrical engineers and computer scientists on problems arising from formal verification of digital circuits. In fact, algebraic modelling of formal verification problems is developed on the word-level as well as on the bit-level. The word-level model leads to Groebner basis in the polynomial ring over Z/2n while the bit-level model leads to Boolean Groebner bases. In addition to the theoretical foundations of both approaches, the algorithms have been implemented. Using these implementations we show that special data structures and the exploitation of symmetries make Groebner bases competitive to state-of-the-art tools from formal verification but having the advantage of being systematic and more flexible.Comment: 44 pages, 8 figures, submitted to the Special Issue of the Journal of Pure and Applied Algebr

Entwicklung und Etablierung eines Benchmarking zur Optimierung des heimischen Bio-Kartoffelbaues

Zusammenfassung Während die Nachfrage nach ökologisch erzeugten Nahrungsmitteln stetig zunimmt, erwartet der Verbraucher zusehends mehr innere, äußere und sensorische Qualität sowie produktionsspezifische Prozessqualität. Speziell die ökologische Kartoffelproduktion bedarf eines Qualitätsmanagementsystems, welches die Landwirte bei der Erreichung einer ganzen Reihe von Qualitätsparametern unterstützt. Das Kartoffel-Qualitätsmanagement (QM) wurde entwickelt, um dieses Ziel durch direkt aus der Praxis gewonnene Daten zu erreichen. So werden jährlich von allen beteiligten Landwirten Kartoffelproben bonitiert und die Produktionsdaten detailliert dokumentiert. Einmal jährlich besucht der Kartoffelbauberater die Landwirte für ein "Audit", welches für die korrekte Datenerfassung und die gemeinsame Reflexion der aktuellen Anbauperiode wichtig ist. Alle Daten werden in einer Internetdatenbank verwaltet, auf welche jeder beteiligte Landwirt mit einem eigenen “Account“ zugreifen kann. Im Auswertungsteil des Programms kann der Landwirt eigene Qualitätsdaten mit den anonymisierten Daten aller Teilnehmer vergleichen. Somit kann er Stärken und Schwächen seiner eigenen Produktionsmethodik herausarbeiten. Zusätzlich werden die gesamten Daten durch die Fachberatung analysiert und wichtige Ergebnisse an die Landwirte weitergereicht. Erste Auswertungen zeigten, dass interessante Zusammenhänge zwischen bestimmten Produktionsdaten und Qualitätsergebnissen bereits bei einem kleinen Datenumfang abgeleitet werden können. Es ist anzunehmen, dass Aussagekraft und Repräsentativität dieser Ergebnisse mit wachsendem Datenumfang zunehmen

Background Summarization of Event Timelines

Generating concise summaries of news events is a challenging natural language processing task. While journalists often curate timelines to highlight key sub-events, newcomers to a news event face challenges in catching up on its historical context. In this paper, we address this need by introducing the task of background news summarization, which complements each timeline update with a background summary of relevant preceding events. We construct a dataset by merging existing timeline datasets and asking human annotators to write a background summary for each timestep of each news event. We establish strong baseline performance using state-of-the-art summarization systems and propose a query-focused variant to generate background summaries. To evaluate background summary quality, we present a question-answering-based evaluation metric, Background Utility Score (BUS), which measures the percentage of questions about a current event timestep that a background summary answers. Our experiments show the effectiveness of instruction fine-tuned systems such as Flan-T5, in addition to strong zero-shot performance using GPT-3.5.Comment: EMNLP 2023 camera-read

On Conditional and Compositional Language Model Differentiable Prompting

Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts can be represented by a human-engineered word sequence or by a learned continuous embedding. In this work, we investigate conditional and compositional differentiable prompting. We propose a new model, Prompt Production System (PRopS), which learns to transform task instructions or input metadata, into continuous prompts that elicit task-specific outputs from the PLM. Our model uses a modular network structure based on our neural formulation of Production Systems, which allows the model to learn discrete rules -- neural functions that learn to specialize in transforming particular prompt input patterns, making it suitable for compositional transfer learning and few-shot learning. We present extensive empirical and theoretical analysis and show that PRopS consistently surpasses other PLM adaptation techniques, and often improves upon fully fine-tuned models, on compositional generalization tasks, controllable summarization and multilingual translation, while needing fewer trainable parameters.Comment: Accepted at International Joint Conference on Artificial Intelligence (IJCAI) 202

Én fugl i hånden er bedre enn ti i boka

I denne kvalitative studien ser jeg nærmere på hvordan én fugl i elevens hånd kan bidra til å engasjere i naturfag. Utvalget består av en 10. klasse hvor jeg som forsker har analysert tre selvlagde undervisningsøkter i naturfag. Ved bruk av intervju og tematisk analyse av disse har jeg fått en forståelse av hva som engasjerer elever i naturfag og hvordan nærmiljøet og ringmerking av ville fugler kan skape engasjement. Tittelen på oppgaven er inspirert av et kjent ordtak og hinter til min konklusjon om at én fugl i hånden engasjerer mer enn ti fuglebilder i en bok. Dette baserer jeg på funn fra intervjuene sett i sammenheng med forskning om hvordan praktisk arbeid og håndtering av dyr påvirker elevers lærelyst, og hvordan stimulering av flere sanser kan gjøre det enklere for elever å få en forståelse for naturen. Mine funn har jeg begrenset til elevenes opplevelser av å håndtere og erfare fugler i egne hender, læringsarenaen som naturen i nærmiljøet er, og behovet for individuelle tilpasninger gjennom aktivitetsvariasjon. Elevene kommer med rike beskrivelser av hva det gjør med dem å få håndtere fugler og hvordan dette er en unik opplevelse som de omtaler som minneverdig og spennende. Elevene forteller om det positive ved å få være ute i frisk luft og at de erfarer en større forståelse av biologiske sammenhenger i naturen rundt dem. Elevene setter også stor pris på å få være delaktige i undervisningen, hvor de får erfare i stedet for å være publikum, og at det er spennende å delta i noe unikt og annerledes. Basert på mine funn kan jeg si at én fugl i elevens hånd kan bidra til å engasjere i naturfag ved å gi elevene førstehåndsinformasjon og sterke opplevelser, som de forteller at de ikke ville fått ved utelukkende bruk av bøker. Arbeid med levende fugler i naturlige omgivelser har bidratt til å bygge egeninteresse og oppdagelse av engasjement i naturfag