29 research outputs found
Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020
This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge. A particular question we are interested in answering is how well neural methods, and specifically transformer models, such as BERT, perform on the answer type prediction task compared to traditional approaches. Our main finding is that coarse-grained answer types can be identified effectively with standard text classification methods, with over 95% accuracy, and BERT can bring only marginal improvements. For fine-grained type detection, on the other hand, BERT clearly outperforms previous retrieval-based approaches.publishedVersio
Report on the 44th European Conference on Information Retrieval (ECIR 2022): The First Major Hybrid IR Conference
The 44th European Conference on Information Retrieval (ECIR’22) was held in Stavanger, Norway. It represents a landmark, not only for being the northernmost ECIR ever, but also for being the first major IR conference in a hybrid format. This article reports on ECIR’22 from the organizers’ perspective, with a particular emphasis on elements of the hybrid setup, with the aim to serve as a reference and guidance for future hybrid conferences.publishedVersio
Context Aware Query Rewriting for Text Rankers using LLM
Query rewriting refers to an established family of approaches that are
applied to underspecified and ambiguous queries to overcome the vocabulary
mismatch problem in document ranking. Queries are typically rewritten during
query processing time for better query modelling for the downstream ranker.
With the advent of large-language models (LLMs), there have been initial
investigations into using generative approaches to generate pseudo documents to
tackle this inherent vocabulary gap. In this work, we analyze the utility of
LLMs for improved query rewriting for text ranking tasks. We find that there
are two inherent limitations of using LLMs as query re-writers -- concept drift
when using only queries as prompts and large inference costs during query
processing. We adopt a simple, yet surprisingly effective, approach called
context aware query rewriting (CAR) to leverage the benefits of LLMs for query
understanding. Firstly, we rewrite ambiguous training queries by context-aware
prompting of LLMs, where we use only relevant documents as context.Unlike
existing approaches, we use LLM-based query rewriting only during the training
phase. Eventually, a ranker is fine-tuned on the rewritten queries instead of
the original queries during training. In our extensive experiments, we find
that fine-tuning a ranker using re-written queries offers a significant
improvement of up to 33% on the passage ranking task and up to 28% on the
document ranking task when compared to the baseline performance of using
original queries
Query Understanding in the Age of Large Language Models
Querying, conversing, and controlling search and information-seeking
interfaces using natural language are fast becoming ubiquitous with the rise
and adoption of large-language models (LLM). In this position paper, we
describe a generic framework for interactive query-rewriting using LLMs. Our
proposal aims to unfold new opportunities for improved and transparent intent
understanding while building high-performance retrieval systems using LLMs. A
key aspect of our framework is the ability of the rewriter to fully specify the
machine intent by the search engine in natural language that can be further
refined, controlled, and edited before the final retrieval phase. The ability
to present, interact, and reason over the underlying machine intent in natural
language has profound implications on transparency, ranking performance, and a
departure from the traditional way in which supervised signals were collected
for understanding intents. We detail the concept, backed by initial
experiments, along with open questions for this interactive query understanding
framework.Comment: Accepted to GENIR(SIGIR'23
Making sense of nonsense : Integrated gradient-based input reduction to improve recall for check-worthy claim detection
Analysing long text documents of political discourse to identify check-worthy claims (claim detection) is known to be an important task in automated fact-checking systems, as it saves the precious time of fact-checkers, allowing for more fact-checks. However, existing methods use black-box deep neural NLP models to detect check-worthy claims, which limits the understanding of the model and the mistakes they make. The aim of this study is therefore to leverage an explainable neural NLP method to improve the claim detection task. Specifically, we exploit well known integrated gradient-based input reduction on textCNN and BiLSTM to create two different reduced claim data sets from ClaimBuster. We observe that a higher recall in check-worthy claim detection is achieved on the data reduced by BiLSTM compared to the models trained on claims. This is an important remark since the cost of overlooking check-worthy claims is high in claim detection for fact-checking. This is also the case when a pre-trained BERT sequence classification model is fine-tuned on the reduced data set. We argue that removing superfluous tokens using explainable NLP could unlock the true potential of neural language models for claim detection, even though the reduced claims might make no sense to humans. Our findings provide insights on task formulation, design of annotation schema and data set preparation for check-worthy claim detection.publishedVersio
Trustworthy journalism through AI
Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust