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Language-enabled AI systems can answer complex, multi-hop questions to high accuracy, but supporting answers with evidence is a more challenging task which is important for the transparency and trustworthiness to users. Prior work in this area typically makes a trade-off between efficiency and accuracy; state-of-the-art deep neural network systems are too cumbersome to be useful in large-scale applications, while the fastest systems lack reliability. In this work, we integrate fast syntactic methods with powerful semantic methods for multi-hop explanation generation based on declarative facts. Our best system, which learns a lightweight operation to simulate multi-hop reasoning over pieces of evidence and fine-tunes language models to re-rank generated explanation chains, outperforms a purely syntactic baseline from prior work by up to 7% in gold explanation retrieval rate.

Information retrieval is an important component in natural language processing, for knowledge intensive tasks such as question answering and fact checking. Recently, information retrieval has seen the emergence of dense retrievers, based on neural networks, as an alternative to classical sparse methods based on term-frequency. These models have obtained state-of-the-art results on datasets and tasks where large training sets are available. However, they do not transfer well to new domains or applications with no training data, and are often outperformed by term-frequency methods such as BM25 which are not supervised. Thus, a natural question is whether it is possible to train dense retrievers without supervision. In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers, and show that it leads to strong retrieval performance. More precisely, we show on the BEIR benchmark that our model outperforms BM25 on 11 out of 15 datasets. Furthermore, when a few thousands examples are available, we show that fine-tuning our model on these leads to strong improvements compared to BM25. Finally, when used as pre-training before fine-tuning on the MS-MARCO dataset, our technique obtains state-of-the-art results on the BEIR benchmark.

We present a framework for improving the performance of a wide class of retrieval models at minimal computational cost. It utilizes precomputed document representations extracted by a base dense retrieval method and involves training a model to jointly score a large set of retrieved candidate documents for each query, while potentially transforming on the fly the representation of each document in the context of the other candidates as well as the query itself. When scoring a document representation based on its similarity to a query, the model is thus aware of the representation of its "peer" documents. We show that our approach leads to substantial improvement in retrieval performance over the base method and over scoring candidate documents in isolation from one another, as in a pair-wise training setting. Crucially, unlike term-interaction rerankers based on BERT-like encoders, it incurs a negligible computational overhead on top of any first-stage method at run time, allowing it to be easily combined with any state-of-the-art dense retrieval method. Finally, concurrently considering a set of candidate documents for a given query enables additional valuable capabilities in retrieval, such as score calibration and mitigating societal biases in ranking.

The main focus of information retrieval methods is to provide accurate and efficient results which are cost-effective too. LINGO (Label Induction Grouping Algorithm) is a clustering algorithm that aims to provide search results in form of quality clusters but also has a few limitations. In this paper, our focus is based on achieving results that are more meaningful and improving the overall performance of the algorithm. LINGO works on two main steps; Cluster Label Induction by using Latent Semantic Indexing technique (LSI) and Cluster content discovery by using the Vector Space Model (VSM). As LINGO uses VSM in cluster content discovery, our task is to replace VSM with LSI for cluster content discovery and to analyze the feasibility of using LSI with Okapi BM25. The next task is to compare the results of a modified method with the LINGO original method. The research is applied to five different text-based data sets to get more reliable results for every method. Research results show that LINGO produces 40-50% better results when using LSI for content Discovery. From theoretical evidence using Okapi BM25 for scoring method in LSI (LSI+Okapi BM25) for cluster content discovery instead of VSM, also results in better clusters generation in terms of scalability and performance when compares to both VSM and LSI's Results.

Ecosia, the search engine that uses its profits to plant trees, is launching a new shopping feature. The company, which was founded in 2009, donates its expendable funds to tree-planting organizations. It claims to have planted 130 million trees across 30 countries around the world. Ecosia Shopping recommends products on Amazon, Kelkoo and Idealo, and other shopping partners that have been sustainably made, are reused, or have been refurbished. The feature is available now in the UK, Germany and France.

We propose value retrieval with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of layout and semantics of a form. To further boost model performance, we propose a simple document language modeling (simpleDLM) strategy to improve document understanding on large-scale model pre-training. Experimental results show that our method outperforms our baselines significantly and the simpleDLM further improves our performance on value retrieval by around 17\% F1 score compared with the state-of-the-art pre-training method. Code will be made publicly available.

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

Previous work on Chinese Knowledge Base Question Answering has been restricted due to the lack of complex Chinese semantic parsing dataset and the exponentially growth of searching space with the length of relation paths. This paper proposes an efficient pipeline method equipped with a pre-trained language model and a strategy to construct artificial training samples, which only needs small amount of data but performs well on open-domain complex Chinese Question Answering task. Besides, By adopting a Beam Search algorithm based on a language model marking scores for candidate query tuples, we decelerate the growing relation paths when generating multi-hop query paths. Finally, we evaluate our model on CCKS2019 Complex Question Answering via Knowledge Base task and achieves F1-score of 62.55\% on the test dataset. Moreover when training with only 10\% data, our model can still achieves F1-score of 58.54\%. The result shows the capability of our model to process KBQA task and the advantage in few-shot learning.

Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.