Using semantic cues to learn syntax

We present a method for dependency grammar induction that utilizes sparse annotations of semantic relations. This induction set-up is attractive because such annotations provide useful clues about the underlying syntactic structure, and they are readily available in many domains (e.g., info-boxes and HTML markup). Our method is based on the intuition that syntactic realizations of the same semantic predicate exhibit some degree of consistency. We incorporate this intuition in a directed graphical model that tightly links the syntactic and semantic structures. This design enables us to exploit syntactic regularities while still allowing for variations. Another strength of the model lies in its ability to capture non-local dependency relations. Our results demonstrate that even a small amount of semantic annotations greatly improves the accuracy of learned dependencies when tested on both in-domain and out-of-domain texts.United States. Defense Advanced Research Projects Agency (Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0172)United States. Defense Advanced Research Projects Agency (Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0172)U.S. Army Research Laboratory (contract no. W911NF-10-1-0533

Selective Sharing for Multilingual Dependency Parsing

We present a novel algorithm for multilingual dependency parsing that uses annotations from a diverse set of source languages to parse a new unannotated language. Our motivation is to broaden the advantages of multilingual learning to languages that exhibit significant differences from existing resource-rich languages. The algorithm learns which aspects of the source languages are relevant for the target language and ties model parameters accordingly. The model factorizes the process of generating a dependency tree into two steps: selection of syntactic dependents and their ordering. Being largely language-universal, the selection component is learned in a supervised fashion from all the training languages. In contrast, the ordering decisions are only influenced by languages with similar properties. We systematically model this cross-lingual sharing using typological features. In our experiments, the model consistently outperforms a state-of-the-art multilingual parser. The largest improvement is achieved on the non Indo-European languages yielding a gain of 14.4%.National Science Foundation (U.S.) (IIS-0835445)United States. Multidisciplinary University Research Initiative (W911NF-10-1-0533)United States. Defense Advanced Research Projects Agency. Broad Operational Language Translatio

In-domain relation discovery with meta-constraints via posterior regularization

We present a novel approach to discovering relations and their instantiations from a collection of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single relation should exhibit regularities at multiple levels of linguistic structure, including lexicography, syntax, and document-level context. We capture these regularities via the structure of our probabilistic model as well as a set of declaratively-specified constraints enforced during posterior inference. Across two domains our approach successfully recovers hidden relation structure, comparable to or outperforming previous state-of-the-art approaches. Furthermore, we find that a small set of constraints is applicable across the domains, and that using domain-specific constraints can further improve performance.United States. Defense Advanced Research Projects Agency (Machine Reading Program under Air Force Research Laboratory (AFRL) prime contract no. FA8750-09-C-0172

Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases

Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing

The sliding window approach provides an elegant way to handle contexts of sizes larger than the Transformer's input window, for tasks like language modeling. Here we extend this approach to the sequence-to-sequence task of document parsing. For this, we exploit recent progress in transition-based parsing to implement a parser with synchronous sliding windows over source and target. We develop an oracle and a parser for document-level AMR by expanding on Structured-BART such that it leverages source-target alignments and constrains decoding to guarantee synchronicity and consistency across overlapping windows. We evaluate our oracle and parser using the Abstract Meaning Representation (AMR) parsing 3.0 corpus. On the Multi-Sentence development set of AMR 3.0, we show that our transition oracle loses only 8\% of the gold cross-sentential links despite using a sliding window. In practice, this approach also results in a high-quality document-level parser with manageable memory requirements. Our proposed system performs on par with the state-of-the-art pipeline approach for document-level AMR parsing task on Multi-Sentence AMR 3.0 corpus while maintaining sentence-level parsing performance

AMR Parsing with Instruction Fine-tuned Pre-trained Language Models

Instruction fine-tuned language models on a collection of instruction annotated datasets (FLAN) have shown highly effective to improve model performance and generalization to unseen tasks. However, a majority of standard parsing tasks including abstract meaning representation (AMR), universal dependency (UD), semantic role labeling (SRL) has been excluded from the FLAN collections for both model training and evaluations. In this paper, we take one of such instruction fine-tuned pre-trained language models, i.e. FLAN-T5, and fine-tune them for AMR parsing. Our extensive experiments on various AMR parsing tasks including AMR2.0, AMR3.0 and BioAMR indicate that FLAN-T5 fine-tuned models out-perform previous state-of-the-art models across all tasks. In addition, full fine-tuning followed by the parameter efficient fine-tuning, LoRA, further improves the model performances, setting new state-of-the-arts in Smatch on AMR2.0 (86.4), AMR3.0 (84.9) and BioAMR (82.3)

Comparison of efficacy and safety of intramuscular magnesium sulphate with low dose intravenous regimen in treatment of eclampsia

INTRODUCTION: Eclampsia contributes to maternal mortality in developing, underdeveloped world. Various drugs have been tried to treat eclampsia. Magnesium sulphate has become the drug of choice due to various advantages and is associated with adverse outcome for both mother and fetus if not used correctly. OBJECTIVE: To compare the efficacy and safety of intramuscular magnesium sulphate with low dose intravenous regimen in treatment of eclampsia STUDY SETTING: The study was conducted at Gynecology and Obstetrics Department unit II, Holy Family Hospital, Rawalpindi, from June 20, 2020 to December 20, 2020. Study design was Randomized Controlled Trial. SUBJECTS & METHODS: Patients were randomly distributed into two groups, group-A (IM Group) and group-B (IV Group). Group-A patients received a loading dose of 4 gm IV MgSO4 over 5-10 minutes+10 gm MgSO4 deep intra-muscular injection (5 gm in each buttock) and a maintenance dose of 5 gm MgSO4 deep intramuscular injection in alternate buttock every 4 hourly. Group-B patients received MgSO4 4 gm slow IV over 5-10 minutes as loading dose and 1 gm MgSO4 per hour as continuous intravenous maintenance infusion. Clinical response to therapy for both drugs was calculated in terms of efficacy and safety. All the data were entered & analyzed by using SPSS v25.0. Both the groups were compared in terms of efficacy and safety by using Chi-Square test. A p-value less than 0.05  was taken as significant. RESULTS: A total of 160 patients with eclampsia were enrolled for this study. Patients were divided into two groups i.e. Group-A (IM MgSO4) and Group-B (IV MgSO4). In group-A, there were 45(56.3%) in 18-30 years age group and 35(43.8%) in 31-40 years age group, while in group-B, there were 48(60.0%) in 18-30 years age group and 32(40.0%) in 31-40 years age group. In IM MgSO4 group, prevention from recurrence of seizure was noted in 74(92.5%) and 78(97.5%) in IV MgSO4 group, which is statistically insignificant with a p-value of 0.147. CONCLUSION: Both IM and IV regimen are equally effective in controlling the recurrence of convulsions. IM Magnesium Sulphate is associated with a higher incidence of toxicity as evidenced by significantly higher incidence of loss of knee jerk reflex. Both IM and IV regimen are equally effective but IM Magnesium Sulphate is associated with a higher incidence of toxicity. KEY WORDS: Eclampsia, Intramuscular MgSO4, Intravenous MgSO4

Bootstrapping Multilingual AMR with Contextual Word Alignments

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision. Weachieve this goal by bootstrapping transformer-based multilingual word embeddings, in partic-ular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique forforeign-text-to-English AMR alignment, usingthe contextual word alignment between En-glish and foreign language tokens. This wordalignment is weakly supervised and relies onthe contextualized XLM-R word embeddings.We achieve a highly competitive performancethat surpasses the best published results forGerman, Italian, Spanish and Chinese