Despite recent breakthroughs in Machine Learning for Natural Language Processing, the Natural Language Inference (NLI) problems still constitute a challenge. To this purpose, we contribute a new dataset that focuses exclusively on the factivity phenomenon; however, our task remains the same as other NLI tasks, that is prediction of entailment, contradiction, or neutral (ECN). In this paper, we describe the LingFeatured NLI corpus and present the results of analyses designed to characterize the factivity/non-factivity opposition in natural language. The dataset contains entirely natural language utterances in Polish and gathers 2432 verb-complement pairs and 309 unique verbs. The dataset is based on the National Corpus of Polish (NKJP) and is a representative subcorpus in regard to syntactic construction [V][że][cc]. We also present an extended version of the set (3035 sentences) consisting more sentences with internal negations. We prepared deep learning benchmarks for both sets. We found that transformer BERT-based models working on sentences obtained relatively good results ($\approx 89\%$ F1 score on base dataset). Even though better results were achieved using linguistic features ($\approx 91\%$ F1 score on base dataset), this model requires more human labor (humans in the loop) because features were prepared manually by expert linguists. BERT-based models consuming only the input sentences show that they capture most of the complexity of NLI/factivity. Complex cases in the phenomenon—for example, cases with entitlement (E) and non-factive verbs—still remain an open issue for further research.

Abstract meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish.

In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While the field of NER has been studied extensively in recent years, the mechanism of efficiently recognizing named entities (NEs) in Korean has hardly been explored. This is because the Korean language has distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme for Korean corpora by adopting the CoNLL-U format, which decomposes Korean words into morphemes and reduces the ambiguity of NEs in the original segmentation that may contain functional morphemes such as postpositions and particles, is proposed herein. We investigate how the NE tags are best represented in this morpheme-based scheme and implement an algorithm to convert word-based and syllable-based Korean corpora with NEs into the proposed morpheme-based format. Analyses of the results of traditional and neural models reveal that the proposed morpheme-based format is feasible, and the varied performances of the models under the influence of various additional language-specific features are demonstrated. Extrinsic conditions were also considered to observe the variance of the performances of the proposed models, given different types of data, including the original segmentation and different types of tagging formats.

A lot has happened since OpenAI released ChatGPT to the public in November 2022. We review how things unfolded over the course of the year, tracking significant events and announcements from the tech giants leading the generative AI race and from other players of note; along the way we note the wider impacts of the technology’s progress.

We suggest that foundation models are general purpose solutions similar to general purpose programmable microprocessors, where fine-tuning and prompt-engineering are analogous to coding for microprocessors. Evaluating general purpose solutions is not like hypothesis testing. We want to know how well the machine will perform on an unknown program with unknown inputs for unknown users with unknown budgets and unknown utility functions. This paper is based on an invited talk by John Mashey, “Lessons from SPEC,” at an ACL-2021 workshop on benchmarking. Mashey started by describing Standard Performance Evaluation Corporation (SPEC), a benchmark that has had more impact than benchmarks in our field because SPEC addresses an import commercial question: which CPU should I buy? In addition, SPEC can be interpreted to show that CPUs are 50,000 faster than they were 40 years ago. It is remarkable that we can make such statements without specifying the program, users, task, dataset, etc. It would be desirable to make quantitative statements about improvements of general purpose foundation models over years/decades without specifying tasks, datasets, use cases, etc.

Usage of large language models and chat bots will almost surely continue to grow, since they are so easy to use, and so (incredibly) credible. I would be more comfortable with this reality if we encouraged more evaluations with humans-in-the-loop to come up with a better characterization of when the machine can be trusted and when humans should intervene. This article will describe a homework assignment, where I asked my students to use tools such as chat bots and web search to write a number of essays. Even after considerable discussion in class on hallucinations, many of the essays were full of misinformation that should have been fact-checked. Apparently, it is easier to believe ChatGPT than to be skeptical. Fact-checking and web search are too much trouble.

Retrieval-augmented generation (RAG) adds a simple but powerful feature to chatbots, the ability to upload files just-in-time. Chatbots are trained on large quantities of public data. The ability to upload files just-in-time makes it possible to reduce hallucinations by filling in gaps in the knowledge base that go beyond the public training data such as private data and recent events. For example, in a customer service scenario, with RAG, we can upload your private bill and then the bot can discuss questions about your bill as opposed to generic FAQ questions about bills in general. This tutorial will show how to upload files and generate responses to prompts; see https://github.com/kwchurch/RAG for multiple solutions based on tools from OpenAI, LangChain, HuggingFace transformers and VecML.

This article reports on designing and implementing a multiclass sentiment classification approach to handle the imbalanced class distribution of Arabic documents. The proposed approach, sentiment classification of Arabic documents (SCArD), combines the advantages of a clustering-based undersampling (CBUS) method and an ensemble learning model to aid machine learning (ML) classifiers in building accurate models against highly imbalanced datasets. The CBUS method applies two standard clustering algorithms: K-means and expectation–maximization, to balance the ratio between the major and the minor classes by decreasing the number of the major class instances and maintaining the number of the minor class instances at the cluster level. The merits of the proposed approach are that it does not remove the majority class instances from the dataset nor injects the dataset with artificial minority class instances. The resulting balanced datasets are used to train two ML classifiers, random forest and updateable Naïve Bayes, to develop prediction data models. The best prediction data models are selected based on F1-score rates. We applied two techniques to test SCArD and generate new predictions from the imbalanced test dataset. The first technique uses the best prediction data models. The second technique uses the majority voting ensemble learning model, which combines the best prediction data models to generate the final predictions. The experimental results showed that SCArD is promising and outperformed the other comparative classification models based on the F1-score rates.

Morphological re-inflection generation is one of the most challenging tasks in the natural language processing (NLP) domain, especially with morphologically rich, low-resource languages like Arabic. In this research, we investigate the ability of transformer-based models in the singular-to-plural Arabic noun conversion task. We start with pretraining a Character-BERT model on a masked language modeling task using 1,134,950 Arabic words and then adopting the fusion technique to transfer the knowledge gained by the pretrained model to a full encoder–decoder transformer model, in one of the proposed settings. The second proposed setting directly fuses the output Character-BERT embeddings into the decoder. We then analyze and compare the performance of the two architectures and provide an interpretability section in which we track the features of attention with respect to the model. We perform the interpretation on both the macro and micro levels, providing some individual examples. Moreover, we provide a thorough error analysis showing the strengths and weaknesses of the proposed framework. To the best of our knowledge, this is the first effort in the Arabic NLP domain that adopts the development of an end-to-end fused-transformer deep learning model to address the problem of singular-to-plural conversion.

The technical and mainstream media’s headline coverage of AI invariably centers around the often astounding abilities demonstrated by large language models. That’s hardly surprising, since to all intents and purposes that’s where the newsworthy magic of generative AI lies. But it takes a village to raise a child: behind the scenes, there’s an entire ecosystem that supports the development and deployment of these models and the applications that are built on top of them. Some parts of that ecosystem are dominated by the Big Tech incumbents, but there are also many niches where start-ups are aiming to gain a foothold. We take a look at some components of that ecosystem, with a particular focus on ideas that have led to investment in start-ups over the last year or so.

It’s less than a year since OpenAI’s board voted to fire Sam Altman as CEO, in a palace coup that lasted just a weekend before Altman was reinstated. That weekend and subsequent events in OpenAI’s storyline provide all the ingredients for a soap opera. So, just in case Netflix is interested, here’s a stab at a synopsis of what might be just the first of many seasons of ‘The Generative AI Wars’.