We introduce GRAMMAMT, a grammaticallyaware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences.GRAMMAMT proposes three prompting strategies: gloss-shot, chaingloss and model-gloss.All are training-free, requiring only a few examples that involve minimal effort to collect, and making them wellsuited for low-resource setups.Experiments show that GRAMMAMT enhances translation performance on open-source instruction-tuned LLMs for various low-to high-resource languages across three benchmarks: (1) the largest IGT corpus, (2) the challenging 2023 SIGMOR-PHON Shared Task data over endangered languages, and (3) even in an out-of-domain setting with FLORES.Moreover, ablation studies reveal that leveraging gloss resources could substantially boost MT performance (by over 17 BLEU points) if LLMs accurately generate or access input sentence glosses. Gloss-shotHere are some examples of {Swahili} sentences and their corresponding {English} translations: Swahili sentence: (yeye) alimwona (yeye).Gloss: 3SG -

Different languages are known to have typical and distinctive prosodic profiles.However, the majority of work on prosody across languages has been restricted to monolingual discourse contexts.We build on prior studies by asking: how does the nature of the discourse context influence variations in the prosody of monolingual speech?To answer this question, we compare the prosody of spontaneous, conversational monolingual English and Spanish both in monolingual and in multilingual speech settings.For both languages, we find that monolingual speech produced in a monolingual context is prosodically different from that produced in a multilingual context, with more marked differences having increased proximity to multilingual discourse.Our work is the first to incorporate multilingual discourse contexts into the study of native-level monolingual prosody, and has potential downstream applications for the recognition and synthesis of multilingual speech.

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning).Inspired by speech processing, these zero-shot tasks measure whether minimal differences in representation can be reliably detected.This offers a flexible and interpretable alternative to probing.Applied to XLM-R (Conneau et al., 2020a) across pretraining checkpoints and layers, we find that language discrimination declines over training and becomes concentrated in lower layers, while meaning discrimination strengthens over time and stabilizes in deeper layers.We then explore probing tasks, showing some alignment between our metrics and linguistic learning performance.Our results position ABX tasks as a lightweight framework for analyzing the structure of multilingual representations.

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the equivalence between the KG and the text in the dataset. Using cyclic evaluation we find that manually created WebNLG is much better than automatically created TeKGen and T-REx. Informed by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation. We also construct two synthetic datasets using large language models (LLMs), and observe that these are conducive to models that perform significantly well on cyclic generation of text, but less so on cyclic generation of KGs, probably because of a lack of a consistent underlying ontology.

Current research in automatic summarisation is unapologetically anglo-centered-a persistent state-of-affairs, which also predates neural net approaches.High-quality automatic summarisation datasets are notoriously expensive to create, posing a challenge for any language.However, with digitalisation, archiving, and social media advertising of newswire articles, recent work has shown how, with careful methodology application, large-scale datasets can now be simply gathered instead of written.In this paper, we present a large-scale multilingual summarisation dataset containing articles in 92 languages, spread across 28.8 million articles, in more than 35 writing scripts.This is both the largest, most inclusive, existing automatic summarisation dataset, as well as one of the largest, most inclusive, ever published datasets for any NLP task.We present the first investigation on the efficacy of resource building from news platforms in the low-resource language setting.Finally, we provide some first insight on how low-resource language settings impact state-of-the-art automatic summarisation system performance.

Dataset development for automatic summarisation systems is notoriously English-oriented. In this paper we present the first large-scale non-English language dataset specifically curated for automatic summarisation. The document-summary pairs are news articles and manually written summaries in the Danish language. There has previously been no work done to establish a Danish summarisation dataset, nor any published work on the automatic summarisation of Danish. We provide therefore the first automatic summarisation dataset for the Danish language (large-scale or otherwise). To support the comparison of future automatic summarisation systems for Danish, we include system performance on this dataset of strong well-established unsupervised baseline systems, together with an oracle extractive summariser, which is the first account of automatic summarisation system performance for Danish. Finally, we make all code for automatically acquiring the data freely available and make explicit how this technology can easily be adapted in order to acquire automatic summarisation datasets for further languages.

In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.

Consider two competitive machine learning models, one of which was considered state-of-the art, and the other a competitive baseline.Suppose that by just permuting the examples of the training set, say by reversing the original order, by shuffling, or by mini-batching, you could report substantially better/worst performance for the system of your choice, by multiple percentage points.In this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI).We show that for the two central NLI corpora today, the learning process of neural systems is far too sensitive to permutations of the data.In doing so we reopen the question of how to judge a good neural architecture for NLI, given the available dataset and perhaps, further, the soundness of the NLI task itself in its current state.

We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.